@article{ReinhardHelmerichBorasetal.2022,
  author    = {Reinhard, Sebastian and Helmerich, Dominic A. and Boras, Dominik and Sauer, Markus and Kollmannsberger, Philip},
  title     = {ReCSAI: recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy},
  series = {BMC Bioinformatics},
  volume    = {23},
  journal   = {BMC Bioinformatics},
  number    = {1},
  doi       = {10.1186/s12859-022-05071-5},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-299768},
  year      = {2022},
  abstract  = {Background Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a measured photon distribution. These algorithms make assumptions about the symmetry of the PSF and thus, do not work well with irregular, non-linear PSFs that occur for example in confocal lifetime imaging, where a laser is scanned across the sample. An alternative method for reconstructing sparse emitter sets from noisy, diffraction-limited images is compressed sensing, but due to its high computational cost it has not yet been widely adopted. Deep neural network fitters have recently emerged as a new competitive method for localization microscopy. They can learn to fit arbitrary PSFs, but require extensive simulated training data and do not generalize well. A method to efficiently fit the irregular PSFs from confocal lifetime localization microscopy combining the advantages of deep learning and compressed sensing would greatly improve the acquisition speed and throughput of this method. Results Here we introduce ReCSAI, a compressed sensing neural network to reconstruct localizations for confocal dSTORM, together with a simulation tool to generate training data. We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. We found that a U-Net with a recursive structure inspired by iterative compressed sensing showed the best results on realistic simulated datasets with noise, as well as on real experimentally measured confocal lifetime scanning data. Adding a trainable wavelet denoising layer as prior step further improved the reconstruction quality. Conclusions Our deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisition of multiple frames. In addition, our work offers generic insights on the reconstruction of sparse measurements from noisy experimental data by combining compressed sensing and deep learning. We provide the trained networks, the code for network training and inference as well as the simulation tool as python code and Jupyter notebooks for easy reproducibility.},
  language  = {en}
}
@article{DannhaeuserMrestaniGundelachetal.2022,
  author    = {Dannh{\"a}user, Sven and Mrestani, Achmed and Gundelach, Florian and Pauli, Martin and Komma, Fabian and Kollmannsberger, Philip and Sauer, Markus and Heckmann, Manfred and Paul, Mila M.},
  title     = {Endogenous tagging of Unc-13 reveals nanoscale reorganization at active zones during presynaptic homeostatic potentiation},
  series = {Frontiers in Cellular Neuroscience},
  volume    = {16},
  journal   = {Frontiers in Cellular Neuroscience},
  issn      = {1662-5102},
  doi       = {10.3389/fncel.2022.1074304},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-299440},
  year      = {2022},
  abstract  = {Introduction Neurotransmitter release at presynaptic active zones (AZs) requires concerted protein interactions within a dense 3D nano-hemisphere. Among the complex protein meshwork the (M)unc-13 family member Unc-13 of Drosophila melanogaster is essential for docking of synaptic vesicles and transmitter release. Methods We employ minos-mediated integration cassette (MiMIC)-based gene editing using GFSTF (EGFP-FlAsH-StrepII-TEV-3xFlag) to endogenously tag all annotated Drosophila Unc-13 isoforms enabling visualization of endogenous Unc-13 expression within the central and peripheral nervous system. Results and discussion Electrophysiological characterization using two-electrode voltage clamp (TEVC) reveals that evoked and spontaneous synaptic transmission remain unaffected in unc-13\(^{GFSTF}\) 3rd instar larvae and acute presynaptic homeostatic potentiation (PHP) can be induced at control levels. Furthermore, multi-color structured-illumination shows precise co-localization of Unc-13\(^{GFSTF}\), Bruchpilot, and GluRIIA-receptor subunits within the synaptic mesoscale. Localization microscopy in combination with HDBSCAN algorithms detect Unc-13\(^{GFSTF}\) subclusters that move toward the AZ center during PHP with unaltered Unc-13\(^{GFSTF}\) protein levels.},
  language  = {en}
}
@article{MarquardtKollmannsbergerKrebsetal.2022,
  author    = {Marquardt, Andr{\´e} and Kollmannsberger, Philip and Krebs, Markus and Argentiero, Antonella and Knott, Markus and Solimando, Antonio Giovanni and Kerscher, Alexander Georg},
  title     = {Visual clustering of transcriptomic data from primary and metastatic tumors — dependencies and novel pitfalls},
  series = {Genes},
  volume    = {13},
  journal   = {Genes},
  number    = {8},
  issn      = {2073-4425},
  doi       = {10.3390/genes13081335},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-281872},
  year      = {2022},
  abstract  = {Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP)) on three datasets of metastases (n = 682 samples) with three different data transformations (unprocessed, log10 as well as log10 + 1 transformed values), we visualized potential underlying clusters. Additionally, we analyzed two datasets (n = 616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters.},
  language  = {en}
}
@article{MarquardtHartrampfKollmannsbergeretal.2023,
  author    = {Marquardt, Andr{\´e} and Hartrampf, Philipp and Kollmannsberger, Philip and Solimando, Antonio G. and Meierjohann, Svenja and K{\"u}bler, Hubert and Bargou, Ralf and Schilling, Bastian and Serfling, Sebastian E. and Buck, Andreas and Werner, Rudolf A. and Lapa, Constantin and Krebs, Markus},
  title     = {Predicting microenvironment in CXCR4- and FAP-positive solid tumors — a pan-cancer machine learning workflow for theranostic target structures},
  series = {Cancers},
  volume    = {15},
  journal   = {Cancers},
  number    = {2},
  issn      = {2072-6694},
  doi       = {10.3390/cancers15020392},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-305036},
  year      = {2023},
  abstract  = {(1) Background: C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2) Methods: Using Random Forest (RF) analysis, we searched for entity-independent mRNA and microRNA signatures related to CXCR4 and FAP overexpression in our pan-cancer cohort from The Cancer Genome Atlas (TCGA) database — representing n = 9242 specimens from 29 tumor entities. CXCR4- and FAP-positive samples were assessed via StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA). Findings were validated via correlation analyses in n = 1541 tumor samples. TIMER2.0 analyzed the association of CXCR4 / FAP expression and infiltration levels of immune-related cells. (3) Results: We identified entity-independent CXCR4 and FAP gene signatures representative for the majority of solid cancers. While CXCR4 positivity marked an immune-related microenvironment, FAP overexpression highlighted an angiogenesis-associated niche. TIMER2.0 analysis confirmed characteristic infiltration levels of CD8+ cells for CXCR4-positive tumors and endothelial cells for FAP-positive tumors. (4) Conclusions: CXCR4- and FAP-directed PET imaging could provide a non-invasive decision aid for entity-agnostic treatment of microenvironment in solid malignancies. Moreover, this machine learning workflow can easily be transferred towards other theranostic targets.},
  language  = {en}
}