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16 results on '"Eschbach, Katja"'

1. Temporal and sequential transcriptional dynamics define lineage shifts in corticogenesis

Author

Mukhtar, Tanzila, Breda, Jeremie, Adam, Manal A, Boareto, Marcelo, Grobecker, Pascal, Karimaddini, Zahra, Grison, Alice, Eschbach, Katja, Chandrasekhar, Ramakrishnan, Vermeul, Swen, Okoniewski, Michal, Pachkov, Mikhail, Harwell, Corey C, Atanasoski, Suzana, Beisel, Christian, Iber, Dagmar, Nimwegen, Erik, and Taylor, Verdon

Subjects
Biomedical and Clinical Sciences, Stem Cell Research, Stem Cell Research - Nonembryonic - Non-Human, Regenerative Medicine, Stem Cell Research - Embryonic - Non-Human, Genetics, Neurosciences, Brain Disorders, Intellectual and Developmental Disabilities (IDD), 1.1 Normal biological development and functioning, Underpinning research, Neurological, Animals, Mice, Cell Differentiation, Cell Lineage, Cerebral Cortex, Embryonic Stem Cells, Neural Stem Cells, Neurogenesis, Neurons, cortical development, lineage specification, networks, signaling pathways, transcriptional landscape, Biological Sciences, Information and Computing Sciences, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

The cerebral cortex contains billions of neurons, and their disorganization or misspecification leads to neurodevelopmental disorders. Understanding how the plethora of projection neuron subtypes are generated by cortical neural stem cells (NSCs) is a major challenge. Here, we focused on elucidating the transcriptional landscape of murine embryonic NSCs, basal progenitors (BPs), and newborn neurons (NBNs) throughout cortical development. We uncover dynamic shifts in transcriptional space over time and heterogeneity within each progenitor population. We identified signature hallmarks of NSC, BP, and NBN clusters and predict active transcriptional nodes and networks that contribute to neural fate specification. We find that the expression of receptors, ligands, and downstream pathway components is highly dynamic over time and throughout the lineage implying differential responsiveness to signals. Thus, we provide an expansive compendium of gene expression during cortical development that will be an invaluable resource for studying neural developmental processes and neurodevelopmental disorders.

Published
2022

3. Establishing standardized immune phenotyping of metastatic melanoma by digital pathology

Author

Sobottka, Bettina, Nowak, Marta, Frei, Anja Laura, Haberecker, Martina, Merki, Samuel, Levesque, Mitchell P., Dummer, Reinhard, Moch, Holger, Koelzer, Viktor Hendrik, Aebersold, Rudolf, Ak, Melike, Al-Quaddoomi, Faisal S., Albinus, Jonas, Alborelli, Ilaria, Andani, Sonali, Attinger, Per-Olof, Bacac, Marina, Baumhoer, Daniel, Beck-Schimmer, Beatrice, Beerenwinkel, Niko, Beisel, Christian, Bernasconi, Lara, Bertolini, Anne, Bodenmiller, Bernd, Bonilla, Ximena, Casanova, Ruben, Chevrier, Stéphane, Chicherova, Natalia, D'Costa, Maya, Danenberg, Esther, Davidson, Natalie, Drăganmoch, Monica-Andreea, Engler, Stefanie, Erkens, Martin, Eschbach, Katja, Esposito, Cinzia, Fedier, André, Ferreira, Pedro, Ficek, Joanna, Frey, Bruno, Goetze, Sandra, Grob, Linda, Gut, Gabriele, Günther, Detlef, Haeuptle, Pirmin, Heinzelmann-Schwarz, Viola, Herter, Sylvia, Holtackers, Rene, Huesser, Tamara, Irmisch, Anja, Jacob, Francis, Jacobs, Andrea, Jaeger, Tim M., Jahn, Katharina, James, Alva R., Jermann, Philip M., Kahles, André, Kahraman, Abdullah, Kuebler, Werner, Kuipers, Jack, Kunze, Christian P., Kurzeder, Christian, Lehmann, Kjong-Van, Lugert, Sebastian, Maass, Gerd, Manz, Markus G., Markolin, Philipp, Mena, Julien, Menzel, Ulrike, Metzler, Julian M., Miglino, Nicola, Milani, Emanuela S., Muenst, Simone, Murri, Riccardo, Ng, Charlotte K.Y., Nicolet, Stefan, Pedrioli, Patrick G.A., Pelkmans, Lucas, Piscuoglio, Salvatore, Prummer, Michael, Ritter, Mathilde, Rommel, Christian, Rosano-González, María L., Rätsch, Gunnar, Santacroce, Natascha, del Castillo, Jacobo Sarabia, Schlenker, Ramona, Schwalie, Petra C., Schwan, Severin, Schär, Tobias, Senti, Gabriela, Singer, Franziska, Sivapatham, Sujana, Snijder, Berend, Sreedharan, Vipin T., Stark, Stefan, Stekhoven, Daniel J., Theocharides, Alexandre P.A., Thomas, Tinu M., Tolnay, Markus, Tosevski, Vinko, Toussaint, Nora C., Tuncel, Mustafa A., Tusup, Marina, Van Drogen, Audrey, Vetter, Marcus, Vlajnic, Tatjana, Weber, Sandra, Weber, Walter P., Wegmann, Rebekka, Weller, Michael, Wendt, Fabian, Wey, Norbert, Wicki, Andreas, Wildschut, Mattheus HE, Wollscheid, Bernd, Yu, Shuqing, Ziegler, Johanna, Zimmermann, Marc, Zoche, Martin, and Zuend, Gregor

Published
2021
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6. Single-cell analysis reveals inter- and intratumour heterogeneity in metastatic breast cancer

Author

Hamelin, Baptiste, primary, Obradović, Milan, additional, Sethi, Atul, additional, Kloc, Michal, additional, Muenst, Simone, additional, Beisel, Christian, additional, Eschbach, Katja, additional, Kohler, Hubertus, additional, Soysal, Savas, additional, Vetter, Marcus, additional, Weber, Walter P., additional, Stadler, Michael B., additional, and Bentires-Alj, Mohamed, additional

Published
2022
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8. Temporal and sequential transcriptional dynamics define lineage shifts in corticogenesis

Author

Mukhtar, Tanzila, primary, Breda, Jeremie, additional, Boareto, Marcelo, additional, Grobecker, Pascal, additional, Karimaddini, Zahra, additional, Grison, Alice, additional, Eschbach, Katja, additional, Chandrasekhar, Ramakrishnan, additional, Vermeul, Swen, additional, Okoniewski, Michal, additional, Pachkov, Mikhail, additional, Atanasoski, Suzana, additional, Beisel, Christian, additional, Iber, Dagmar, additional, van Nimwegen, Erik, additional, and Taylor, Verdon, additional

Published
2022
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9. SCIM: universal single-cell matching with unpaired feature sets

Author

Stark, Stefan G, Ficek, Joanna, Locatello, Francesco, Bonilla, Ximena, Chevrier, Stéphane, Singer, Franziska, Aebersold, Rudolf, Al-Quaddoomi, Faisal S, Albinus, Jonas, Alborelli, Ilaria, Andani, Sonali, Attinger, Per-Olof, Bacac, Marina, Baumhoer, Daniel, Beck-Schimmer, Beatrice, Beerenwinkel, Niko, Beisel, Christian, Bernasconi, Lara, Bertolini, Anne, Bodenmiller, Bernd, Casanova, Ruben, Chicherova, Natalia, D'Costa, Maya, Danenberg, Esther, Davidson, Natalie, gan, Monica-Andreea Dră, Dummer, Reinhard, Engler, Stefanie, Erkens, Martin, Eschbach, Katja, Esposito, Cinzia, Fedier, André, Ferreira, Pedro, Frei, Anja L, Frey, Bruno, Goetze, Sandra, Grob, Linda, Gut, Gabriele, Günther, Detlef, Haberecker, Martina, Haeuptle, Pirmin, Heinzelmann-Schwarz, Viola, Herter, Sylvia, Holtackers, Rene, Huesser, Tamara, Irmisch, Anja, Jacob, Francis, Jacobs, Andrea, Jaeger, Tim M, Jahn, Katharina, James, Alva R, Jermann, Philip M, Kahles, André, Kahraman, Abdullah, Koelzer, Viktor H, Kuebler, Werner, Kuipers, Jack, Kunze, Christian P, Kurzeder, Christian, Lehmann, Kjong-Van, Levesque, Mitchell, Lugert, Sebastian, Maass, Gerd, Manz, Markus, Markolin, Philipp, Mena, Julien, Menzel, Ulrike, Metzler, Julian M, Miglino, Nicola, Milani, Emanuela S, Moch, Holger, Muenst, Simone, Murri, Riccardo, Ng, Charlotte KY, Nicolet, Stefan, Nowak, Marta, Pedrioli, Patrick GA, Pelkmans, Lucas, Piscuoglio, Salvatore, Prummer, Michael, Ritter, Mathilde, Rommel, Christian, Rosano-González, María L, Rätsch, Gunnar, Santacroce, Natascha, Castillo, Jacobo Sarabia del, Schlenker, Ramona, Schwalie, Petra C, Schwan, Severin, Schär, Tobias, Senti, Gabriela, Sivapatham, Sujana, Snijder, Berend, Sobottka, Bettina, Sreedharan, Vipin T, Stark, Stefan, Stekhoven, Daniel J, Theocharides, Alexandre PA, Thomas, Tinu M, Tolnay, Markus, Tosevski, Vinko, Toussaint, Nora C, Tuncel, Mustafa A, Tusup, Marina, Drogen, Audrey Van, Vetter, Marcus, Vlajnic, Tatjana, Weber, Sandra, Weber, Walter P, Wegmann, Rebekka, Weller, Michael, Wendt, Fabian, Wey, Norbert, Wicki, Andreas, Wollscheid, Bernd, Yu, Shuqing, Ziegler, Johanna, Zimmermann, Marc, Zoche, Martin, Zuend, Gregor, and University of Zurich

Subjects
Statistics and Probability, 1303 Biochemistry, AcademicSubjects/SCI01060, Computer science, 610 Medicine & health, computer.software_genre, Biochemistry, 03 medical and health sciences, 0302 clinical medicine, Text mining, 1312 Molecular Biology, 1706 Computer Science Applications, Humans, Profiling (information science), 2613 Statistics and Probability, Molecular Biology, 030304 developmental biology, Data, 0303 health sciences, Sequence Analysis, RNA, business.industry, Gene Expression Profiling, Autoencoder, Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, 10032 Clinic for Oncology and Hematology, Bipartite graph, Data mining, Single-Cell Analysis, business, computer, 2605 Computational Mathematics, Algorithms, Software, 030217 neurology & neurosurgery, Data integration, 1703 Computational Theory and Mathematics
Abstract

Motivation: Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results: We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an autoencoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 90% and 78% cell-matching accuracy for each one of the samples, respectively., Bioinformatics, 36 (S2), ISSN:1367-4803, ISSN:1460-2059

Published
2020

10. The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support

Author

Irmisch, Anja, primary, Bonilla, Ximena, additional, Chevrier, Stéphane, additional, Lehmann, Kjong-Van, additional, Singer, Franziska, additional, Toussaint, Nora C., additional, Esposito, Cinzia, additional, Mena, Julien, additional, Milani, Emanuela S., additional, Casanova, Ruben, additional, Stekhoven, Daniel J., additional, Wegmann, Rebekka, additional, Jacob, Francis, additional, Sobottka, Bettina, additional, Goetze, Sandra, additional, Kuipers, Jack, additional, Sarabia del Castillo, Jacobo, additional, Prummer, Michael, additional, Tuncel, Mustafa A., additional, Menzel, Ulrike, additional, Jacobs, Andrea, additional, Engler, Stefanie, additional, Sivapatham, Sujana, additional, Frei, Anja L., additional, Gut, Gabriele, additional, Ficek, Joanna, additional, Miglino, Nicola, additional, Aebersold, Rudolf, additional, Bacac, Marina, additional, Beerenwinkel, Niko, additional, Beisel, Christian, additional, Bodenmiller, Bernd, additional, Dummer, Reinhard, additional, Heinzelmann-Schwarz, Viola, additional, Koelzer, Viktor H., additional, Manz, Markus G., additional, Moch, Holger, additional, Pelkmans, Lucas, additional, Snijder, Berend, additional, Theocharides, Alexandre P.A., additional, Tolnay, Markus, additional, Wicki, Andreas, additional, Wollscheid, Bernd, additional, Rätsch, Gunnar, additional, Levesque, Mitchell P., additional, Ak, Melike, additional, Al-Quaddoomi, Faisal S., additional, Albinus, Jonas, additional, Alborelli, Ilaria, additional, Andani, Sonali, additional, Attinger, Per-Olof, additional, Baumhoer, Daniel, additional, Beck-Schimmer, Beatrice, additional, Bernasconi, Lara, additional, Bertolini, Anne, additional, Chicherova, Natalia, additional, D'Costa, Maya, additional, Danenberg, Esther, additional, Davidson, Natalie, additional, Drăgan, Monica-Andreea, additional, Erkens, Martin, additional, Eschbach, Katja, additional, Fedier, André, additional, Ferreira, Pedro, additional, Frey, Bruno, additional, Grob, Linda, additional, Günther, Detlef, additional, Haberecker, Martina, additional, Haeuptle, Pirmin, additional, Herter, Sylvia, additional, Holtackers, Rene, additional, Huesser, Tamara, additional, Jaeger, Tim M., additional, Jahn, Katharina, additional, James, Alva R., additional, Jermann, Philip M., additional, Kahles, André, additional, Kahraman, Abdullah, additional, Kuebler, Werner, additional, Kunze, Christian P., additional, Kurzeder, Christian, additional, Lugert, Sebastian, additional, Maass, Gerd, additional, Markolin, Philipp, additional, Metzler, Julian M., additional, Muenst, Simone, additional, Murri, Riccardo, additional, Ng, Charlotte K.Y., additional, Nicolet, Stefan, additional, Nowak, Marta, additional, Pedrioli, Patrick G.A., additional, Piscuoglio, Salvatore, additional, Ritter, Mathilde, additional, Rommel, Christian, additional, Rosano-González, María L., additional, Santacroce, Natascha, additional, Schlenker, Ramona, additional, Schwalie, Petra C., additional, Schwan, Severin, additional, Schär, Tobias, additional, Senti, Gabriela, additional, Sreedharan, Vipin T., additional, Stark, Stefan, additional, Thomas, Tinu M., additional, Tosevski, Vinko, additional, Tusup, Marina, additional, Van Drogen, Audrey, additional, Vetter, Marcus, additional, Vlajnic, Tatjana, additional, Weber, Sandra, additional, Weber, Walter P., additional, Weller, Michael, additional, Wendt, Fabian, additional, Wey, Norbert, additional, Wildschut, Mattheus H.E., additional, Yu, Shuqing, additional, Ziegler, Johanna, additional, Zimmermann, Marc, additional, Zoche, Martin, additional, and Zuend, Gregor, additional

Published
2021
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11. SCIM: Universal Single-Cell Matching with Unpaired Feature Sets

Author

Stark, Stefan, Ficek, Joanna, Locatello, Francesco, Bonilla Bustillo, Ximena, Chicherova, Natalia, Singer, Franziska, Tumor Profiler Consortium, Aebersold, Rudolf, Beerenwinkel, Niko, Al-Quaddoomi, Faisal S., Albinus, Jonas, Beisel, Christian, Bertolini, Anne, Davidson, Natalie, Eschbach, Katja, Ferreira, Pedro, Goetze, Sandra, Grob, Linda, Günther, Detlef, Jahn, Katharina, James, Alva R., Kahles, André, Kuipers, Jack, Lehmann, Kjong-Van, Mena, Julien, Menzel, Ulrike, Milani, Emanuela S., Pedrioli, Patrick G.A., Prummer, Michael, Rosano-Gonzalez, Maria L., Rätsch, Gunnar, Schär, Tobias, Snijder, Berend, Thankam Sreedharan, Vipin, Stekhoven, Daniel J., Thomas, Tinu M., Toussaint, Nora C., Tuncel, Mustafa, van Drogen, Audrey, Wegmann, Rebekka, Wendt, Fabian, Wollscheid, Bernd, Yu, Shuqing, and Zimmermann, Marc

Abstract

Motivation Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an auto-encoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 93% and 84% cell-matching accuracy for each one of the samples respectively. Availability https://github.com/ratschlab/scim, bioRxiv

Published
2020

12. Resource misallocation as a mediator of fitness costs in antibiotic resistance

Author

Trauner, Andrej, primary, Banaei-Esfahani, Amir, additional, Gygli, Sebastian M., additional, Warmer, Philipp, additional, Feldmann, Julia, additional, Shafieechashmi, Seyedehsara, additional, Eschbach, Katja, additional, Zampieri, Mattia, additional, Borrell, Sonia, additional, Collins, Ben C., additional, Beisel, Christian, additional, Aebersold, Ruedi, additional, and Gagneux, Sebastien, additional

Published
2018
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16. TGF-β induces oncofetal fibronectin that, in turn, modulates TGF-β superfamily signaling in endothelial cells.

Author

Ventura, Elisa, Weller, Michael, Macnair, Will, Eschbach, Katja, Beisel, Christian, Cordazzo, Cinzia, Claassen, Manfred, Zardi, Luciano, and Burghardt, Isabel

Subjects
GENETIC engineering, GLIOBLASTOMA multiforme treatment, FIBRONECTINS, THERAPEUTICS
Abstract

Gene splicing profiles are frequently altered in cancer, and the splice variants of fibronectin (FN) that contain the extra-domains A (EDA) or B (EDB), referred to as EDA+FN or EDB+FN, are highly upregulated in tumor vasculature. Transforming growth factor β (TGF-β) signaling has been attributed a pivotal role in glioblastoma, with TGF-β promoting angiogenesis and vessel remodeling. By using immunohistochemistry staining, we observed that the oncofetal FN isoforms EDA+FN and EDB+FN are expressed in glioblastoma vasculature. Ex vivo singlecell gene expression profiling of tumors by using CD31 and α-smooth muscle actin (aSMA) as markers for endothelial cells, and pericytes and vascular smooth muscle cells (VSMCs), respectively, confirmed the predominant expression of FN, EDA+FN and EDB+FN in the vascular compartment of glioblastoma. Specifically, within the CD31- positive cell population, we identified a positive correlation between the expression of EDA+FN and EDB+FN, and of molecules associated with TGF-β signaling. Further, TGF-β induced EDA+FN and EDB+FN in human cerebral microvascular endothelial cells and glioblastoma-derived endothelial cells in a SMAD3- and SMAD4- dependent manner. In turn, we found that FN modulated TGF-β superfamily signaling in endothelial cells via the EDA and EDB, pointing towards a bidirectional influence of oncofetal FN and TGF-β superfamily signaling. [ABSTRACT FROM AUTHOR]

Published
2018
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