Cryoelectron tomography enables the visualization of cellular environments in extreme detail through the lens of a benign observer; what remains lacking however are tools to analyze the full amount of information contained within these densely packed volumes. Detailed analysis of macromolecules through subtomogram averaging requires particles to first be localized within the tomogram volume, a task complicated by several factors including a low signal to noise ratio and crowding of the cellular space. Available methods for this task suffer either from being error prone or requiring manual annotation of training data. To assist in this crucial particle picking step, we present TomoTwin: a robust, first in class general picking model for cryo-electron tomograms based on deep metric learning. By embedding tomograms in an information-rich, high-dimensional space which separates macromolecules according to their 3-dimensional structure, TomoTwin allows users to identify proteins in tomograms de novo without manually creating training data or retraining the network each time a new protein is to be located. TomoTwin is open source and available at https://github.com/MPI-Dortmund/tomotwin-cryoet.

Cryogenic-electron tomography enables the visualization of cellular environments in extreme detail, however, tools to analyze the full amount of information contained within these densely packed volumes are still needed. Detailed analysis of macromolecules through subtomogram averaging requires particles to first be localized within the tomogram volume, a task complicated by several factors including a low signal to noise ratio and crowding of the cellular space. Available methods for this task suffer either from being error prone or requiring manual annotation of training data. To assist in this crucial particle picking step, we present TomoTwin: an open source general picking model for cryogenic-electron tomograms based on deep metric learning. By embedding tomograms in an information-rich, high-dimensional space that separates macromolecules according to their three-dimensional structure, TomoTwin allows users to identify proteins in tomograms de novo without manually creating training data or retraining the network to locate new proteins.

BACKGROUND: Ependymomas (EPNs) are the third most common brain tumor in children. These tumors are resistant to available chemotherapeutic treatments, therefore new effective targeted therapeutics must be identified. Increasing evidence shows epigenetic alterations including histone posttranslational modifications (PTMs), are associated with malignancy, chemotherapeutic resistance and prognosis for pediatric EPNs. In this study we examined histone PTMs in EPNs and identified potential targets to improve chemotherapeutic efficacy. METHODS: Global histone H3 lysine 4 trimethylation (H3K4me3) levels were detected in pediatric EPN tumor samples with immunohistochemistry and immunoblots. Candidate genes conferring therapeutic resistance were profiled in pediatric EPN tumor samples with micro-array. Promoter H3K4me3 was examined for two candidate genes, CCND1 and ERBB2, with chromatin-immunoprecipitation coupled with real-time PCR (ChIP-PCR). These methods and MTS assay were used to verify a relationship between H3K4me3 levels and CCND1 and ERBB2, and to investigate cell viability in response to chemotherapeutic drugs in primary cultured pediatric EPN cells. RESULTS: H3K4me3 levels positively correlate with WHO grade malignancy in pediatric EPNs and are associated with progression free survival in patients with posterior fossa group A EPNs (PF-EPN-A). Reduction of H3K4me3 by silencing its methyltransferase SETD1A, in primary cultured EPN cells increased cell response to chemotherapy. CONCLUSIONS: Our results support the development of a novel treatment that targets H3K4me3 to increase chemotherapeutic efficacy in pediatric PF-EPN-A tumors.

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