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4. MPRAnalyze: statistical framework for massively parallel reporter assays

Author

Ashuach, Tal, Fischer, David S, Kreimer, Anat, Ahituv, Nadav, Theis, Fabian J, and Yosef, Nir

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Generic health relevance, Alleles, Biological Assay, Databases, Genetic, Gene Expression Profiling, Genes, Reporter, Hep G2 Cells, High-Throughput Nucleotide Sequencing, Humans, K562 Cells, Software, Statistics as Topic, Environmental Sciences, Information and Computing Sciences, Bioinformatics
Abstract

Massively parallel reporter assays (MPRAs) can measure the regulatory function of thousands of DNA sequences in a single experiment. Despite growing popularity, MPRA studies are limited by a lack of a unified framework for analyzing the resulting data. Here we present MPRAnalyze: a statistical framework for analyzing MPRA count data. Our model leverages the unique structure of MPRA data to quantify the function of regulatory sequences, compare sequences' activity across different conditions, and provide necessary flexibility in an evolving field. We demonstrate the accuracy and applicability of MPRAnalyze on simulated and published data and compare it with existing methods.

Published
2019

9. Learning Tn5 Sequence Bias from ATAC-seq on Naked Chromatin

Author

Ansari, Meshal, Fischer, David S., Theis, Fabian J., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Farkaš, Igor, editor, Masulli, Paolo, editor, and Wermter, Stefan, editor

Published
2020
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10. Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics

Author

Muus, Christoph, Luecken, Malte D., Eraslan, Gökcen, Sikkema, Lisa, Waghray, Avinash, Heimberg, Graham, Kobayashi, Yoshihiko, Vaishnav, Eeshit Dhaval, Subramanian, Ayshwarya, Smillie, Christopher, Jagadeesh, Karthik A., Duong, Elizabeth Thu, Fiskin, Evgenij, Torlai Triglia, Elena, Ansari, Meshal, Cai, Peiwen, Lin, Brian, Buchanan, Justin, Chen, Sijia, Shu, Jian, Haber, Adam L., Chung, Hattie, Montoro, Daniel T., Adams, Taylor, Aliee, Hananeh, Allon, Samuel J., Andrusivova, Zaneta, Angelidis, Ilias, Ashenberg, Orr, Bassler, Kevin, Bécavin, Christophe, Benhar, Inbal, Bergenstråhle, Joseph, Bergenstråhle, Ludvig, Bolt, Liam, Braun, Emelie, Bui, Linh T., Callori, Steven, Chaffin, Mark, Chichelnitskiy, Evgeny, Chiou, Joshua, Conlon, Thomas M., Cuoco, Michael S., Cuomo, Anna S. E., Deprez, Marie, Duclos, Grant, Fine, Denise, Fischer, David S., Ghazanfar, Shila, Gillich, Astrid, Giotti, Bruno, Gould, Joshua, Guo, Minzhe, Gutierrez, Austin J., Habermann, Arun C., Harvey, Tyler, He, Peng, Hou, Xiaomeng, Hu, Lijuan, Hu, Yan, Jaiswal, Alok, Ji, Lu, Jiang, Peiyong, Kapellos, Theodoros S., Kuo, Christin S., Larsson, Ludvig, Leney-Greene, Michael A., Lim, Kyungtae, Litviňuková, Monika, Ludwig, Leif S., Lukassen, Soeren, Luo, Wendy, Maatz, Henrike, Madissoon, Elo, Mamanova, Lira, Manakongtreecheep, Kasidet, Leroy, Sylvie, Mayr, Christoph H., Mbano, Ian M., McAdams, Alexi M., Nabhan, Ahmad N., Nyquist, Sarah K., Penland, Lolita, Poirion, Olivier B., Poli, Sergio, Qi, CanCan, Queen, Rachel, Reichart, Daniel, Rosas, Ivan, Schupp, Jonas C., Shea, Conor V., Shi, Xingyi, Sinha, Rahul, Sit, Rene V., Slowikowski, Kamil, Slyper, Michal, Smith, Neal P., Sountoulidis, Alex, Strunz, Maximilian, Sullivan, Travis B., Sun, Dawei, Talavera-López, Carlos, Tan, Peng, Tantivit, Jessica, Travaglini, Kyle J., Tucker, Nathan R., Vernon, Katherine A., Wadsworth, Marc H., Waldman, Julia, Wang, Xiuting, Xu, Ke, Yan, Wenjun, Zhao, William, and Ziegler, Carly G. K.

Published
2021
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12. Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through ‘reverse phenotyping’

Author

Fischer, David S., Ansari, Meshal, Wagner, Karolin I., Jarosch, Sebastian, Huang, Yiqi, Mayr, Christoph H., Strunz, Maximilian, Lang, Niklas J., D’Ippolito, Elvira, Hammel, Monika, Mateyka, Laura, Weber, Simone, Wolff, Lisa S., Witter, Klaus, Fernandez, Isis E., Leuschner, Gabriela, Milger, Katrin, Frankenberger, Marion, Nowak, Lorenz, Heinig-Menhard, Katharina, Koch, Ina, Stoleriu, Mircea G., Hilgendorff, Anne, Behr, Jürgen, Pichlmair, Andreas, Schubert, Benjamin, Theis, Fabian J., Busch, Dirk H., Schiller, Herbert B., and Schober, Kilian

Published
2021
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15. Programs, Origins, and Niches of Immunomodulatory Myeloid Cells in Gliomas

Author

Miller, Tyler E, primary, El Farran, Chadi Abdul Kader, additional, Couturier, Charles P, additional, Chen, Zeyu, additional, D'Antonio, Joshua P, additional, Verga, Julia, additional, Villanueva, Martin A, additional, Castro, L. Nicolas Gonzalez, additional, Tong, Yuzhou Evelyn, additional, Al-Saadi, Tariq, additional, Chiocca, Andrew N, additional, Fischer, David S, additional, Heiland, Dieter Henrik, additional, Guerriero, Jennifer L, additional, Petrecca, Kevin, additional, Suva, Mario L, additional, Shalek, Alex K, additional, and Bernstein, Bradley E, additional

Published
2023
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18. Probing cell identity hierarchies by fate titration and collision during direct reprogramming

Author

Hersbach, Bob A, Fischer, David S, Masserdotti, Giacomo, Deeksha, Mojžišová, Karolina, Waltzhöni, Thomas, Rodriguez‐Terrones, Diego, Heinig, Matthias, Theis, Fabian J, Götz, Magdalena, and Stricker, Stefan H

Subjects
Computational Theory and Mathematics, General Immunology and Microbiology, EMBO09, EMBO11, Article, Articles, direct reprogramming, Collide-seq, fate conversion, reprogramming, scRNA-seq, Applied Mathematics, Cell Differentiation, Direct Reprogramming, Fate Conversion, Reprogramming, Scrna-seq, Fibroblasts, Cellular Reprogramming, Transcriptome, General Agricultural and Biological Sciences, General Biochemistry, Genetics and Molecular Biology, ddc, Information Systems
Abstract

Despite the therapeutic promise of direct reprogramming, basic principles concerning fate erasure and the mechanisms to resolve cell identity conflicts remain unclear. To tackle these fundamental questions, we established a single-cell protocol for the simultaneous analysis of multiple cell fate conversion events based on combinatorial and traceable reprogramming factor expression: Collide-seq. Collide-seq revealed the lack of a common mechanism through which fibroblast-specific gene expression loss is initiated. Moreover, we found that the transcriptome of converting cells abruptly changes when a critical level of each reprogramming factor is attained, with higher or lower levels not contributing to major changes. By simultaneously inducing multiple competing reprogramming factors, we also found a deterministic system, in which titration of fates against each other yields dominant or colliding fates. By investigating one collision in detail, we show that reprogramming factors can disturb cell identity programs independent of their ability to bind their target genes. Taken together, Collide-seq has shed light on several fundamental principles of fate conversion that may aid in improving current reprogramming paradigms.

Published
2022

21. Cell-Type-Specific Impact of Glucocorticoid Receptor Activation on the Developing Brain: A Cerebral Organoid Study

Author

Cruceanu, Cristiana, primary, Dony, Leander, additional, Krontira, Anthi C., additional, Fischer, David S., additional, Roeh, Simone, additional, Di Giaimo, Rossella, additional, Kyrousi, Christina, additional, Kaspar, Lea, additional, Arloth, Janine, additional, Czamara, Darina, additional, Gerstner, Nathalie, additional, Martinelli, Silvia, additional, Wehner, Stefanie, additional, Breen, Michael S., additional, Koedel, Maik, additional, Sauer, Susann, additional, Sportelli, Vincenza, additional, Rex-Haffner, Monika, additional, Cappello, Silvia, additional, Theis, Fabian J., additional, and Binder, Elisabeth B., additional

Published
2022
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22. Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation

Author

Brunner, Andreas-David, Thielert, Marvin, Vasilopoulou, Catherine, Ammar, Constantin, Coscia, Fabian, Mund, Andreas, Hoerning, Ole B, Bache, Nicolai, Apalategui, Amalia, Lubeck, Markus, Richter, Sabrina, Fischer, David S, Raether, Oliver, Park, Melvin A, Meier, Florian, Theis, Fabian J, Mann, Matthias, Brunner, Andreas-David, Thielert, Marvin, Vasilopoulou, Catherine, Ammar, Constantin, Coscia, Fabian, Mund, Andreas, Hoerning, Ole B, Bache, Nicolai, Apalategui, Amalia, Lubeck, Markus, Richter, Sabrina, Fischer, David S, Raether, Oliver, Park, Melvin A, Meier, Florian, Theis, Fabian J, and Mann, Matthias

Abstract

Single-cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing. However, proteins are the main drivers of cellular function and in-depth characterization of individual cells by mass spectrometry (MS)-based proteomics would thus be highly valuable and complementary. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow-rate chromatography, and a novel trapped ion mobility mass spectrometer, resulting in a more than 10-fold improved sensitivity. We precisely and robustly quantify proteomes and their changes in single, FACS-isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single-cell proteomes to transcriptome data revealed a stable-core proteome despite perturbation, while the transcriptome appears stochastic. Our technology can readily be applied to ultra-high sensitivity analyses of tissue material, posttranslational modifications, and small molecule studies from small cell counts to gain unprecedented insights into cellular heterogeneity in health and disease.

Published
2022

23. Ultra‐high sensitivity mass spectrometry quantifies single‐cell proteome changes upon perturbation

Author

Brunner, Andreas‐David, primary, Thielert, Marvin, additional, Vasilopoulou, Catherine, additional, Ammar, Constantin, additional, Coscia, Fabian, additional, Mund, Andreas, additional, Hoerning, Ole B, additional, Bache, Nicolai, additional, Apalategui, Amalia, additional, Lubeck, Markus, additional, Richter, Sabrina, additional, Fischer, David S, additional, Raether, Oliver, additional, Park, Melvin A, additional, Meier, Florian, additional, Theis, Fabian J, additional, and Mann, Matthias, additional

Published
2022
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24. Ultra2̆010high sensitivity mass spectrometry quantifies single\0̆10cell proteome changes upon perturbation

Author

Brunner, Andreas2̆010David, Thielert, Marvin, Vasilopoulou, Catherine, Ammar, Constantin, Coscia, Fabian, Mund, Andreas, Hoerning, Ole B, Bache, Nicolai, Apalategui, Amalia, Lubeck, Markus, Richter, Sabrina, Fischer, David S, Raether, Oliver, Park, Melvin A, Meier, Florian, Theis, Fabian J, and Mann, Matthias

Subjects
EMBO06, EMBO56, Article, Articles, drug perturbation, low2̆010flow LC-MS, proteomics at single\0̆10cell resolution, single\u1̆0cell heterogeneity, systems biology, ddc
Published
2021

29. Cell-Type-Specific Impact of Glucocorticoid Receptor Activation on the Developing Brain: A Cerebral Organoid Study

Author

Cruceanu, Cristiana Dony, Leander Krontira, Anthi C. Fischer, David S. Roeh, Simone Di Giaimo, Rossella Kyrousi, Christina Kaspar, Lea Arloth, Janine Czamara, Darina Gerstner, Nathalie Martinelli, Silvia Wehner, Stefanie Breen, Michael S. Koedel, Maik Sauer, Susann Sportelli, Vincenza Rex-Haffner, Monika Cappello, Silvia Theis, Fabian J. Binder, Elisabeth B.

Abstract

OBJECTIVE: A fine-tuned balance of glucocorticoid receptor (GR) activation is essential for organ formation, with disturbances influencing many health outcomes. In utero, glucocorticoids have been linked to brain-related negative outcomes, with unclear underlying mechanisms, especially regarding cell-type-specific effects. An in vitro model of fetal human brain development, induced human pluripotent stem cell (hiPSC)-derived cerebral organoids, was used to test whether cerebral organoids are suitable for studying the impact of prenatal glucocorticoid exposure on the developing brain. METHODS: The GR was activated with the synthetic glucocorticoid dexamethasone, and the effects were mapped using single-cell transcriptomics across development. RESULTS: The GR was expressed in all cell types, with increasing expression levels through development. Not only did its activation elicit translocation to the nucleus and the expected effects on known GR-regulated pathways, but also neurons and progenitor cells showed targeted regulation of differentiation- and maturation-related transcripts. Uniquely in neurons, differentially expressed transcripts were significantly enriched for genes associated with behavior-related phenotypes and disorders. This human neuronal glucocorticoid response profile was validated across organoids from three independent hiPSC lines reprogrammed from different source tissues from both male and female donors. CONCLUSIONS: These findings suggest that excessive glucocorticoid exposure could interfere with neuronal maturation in utero, leading to increased disease susceptibility through neurodevelopmental processes at the interface of genetic susceptibility and environmental exposure. Cerebral organoids are a valuable translational resource for exploring the effects of glucocorticoids on early human brain development.

Published
2021

30. Additional file 1 of Sfaira accelerates data and model reuse in single cell genomics

Author

Fischer, David S., Dony, Leander, König, Martin, Moeed, Abdul, Zappia, Luke, Heumos, Lukas, Tritschler, Sophie, Holmberg, Olle, Aliee, Hananeh, and Theis, Fabian J.

Abstract

Additional file 1. Supplementary Figures 1-4. Fig. S1: Zero-shot analysis example cases. Fig. S2: Characterization of the cell type classification task. Fig. S3: Random projection models as baseline models for cell embedding models. Fig. S4 Saliency-based interpretation of models trained on human kidney.

Published
2021
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31. Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through 'reverse phenotyping'

Author

Fischer, David S., Ansari, Meshal, Wagner, Karolin I., Jarosch, Sebastian, Huang, Yiqi, Mayr, Christoph H., Strunz, Maximilian, Lang, Niklas J., D'Ippolito, Elvira, Hammel, Monika, Mateyka, Laura, Weber, Simone, Wolff, Lisa S., Witter, Klaus, Fernandez, Isis E., Leuschner, Gabriela, Milger, Katrin, Frankenberger, Marion, Nowak, Lorenz, Heinig-Menhard, Katharina, Koch, Ina, Stoleriu, Mircea G., Hilgendorff, Anne, Behr, Jürgen, Pichlmair, Andreas, Schubert, Benjamin, Theis, Fabian J., Busch, Dirk H., Schiller, Herbert B., and Schober, Kilian

Subjects
Article, Cellular immunity, SARS-CoV-2, T-cell receptor, Viral infection, ddc
Published
2020

32. Automatic identification of relevant genes from low-dimensional embeddings of single cell RNAseq data

Author

Angerer, Philipp, Fischer, David S., Theis, Fabian J., Scialdone, Antonio, and Marr, Carsten

Subjects
Bioconductor, Identification (information), Computer science, Dimensionality reduction, Principal component analysis, Diffusion map, Embedding, Relevance (information retrieval), Data mining, computer.software_genre, computer
Abstract

Dimensionality reduction is a key step in the analysis of single-cell RNA sequencing data and produces a low-dimensional embedding for visualization and as a calculation base for downstream analysis. Nonlinear techniques are most suitable to handle the intrinsic complexity of large, heterogeneous single cell data. With no linear relation between genes and embedding however, there is no way to extract the identity of genes most relevant for any cell’s position in the low-dimensional embedding, and thus the underlying process.In this paper, we introduce the concepts of global and local gene relevance to compute an equivalent of principal component analysis loadings for non-linear low-dimensional embeddings. While global gene relevance identifies drivers of the overall embedding, local gene relevance singles out genes that change in small, possibly rare subsets of cells. We apply our method to single-cell RNAseq datasets from different experimental protocols and to different low dimensional embedding techniques, shows our method’s versatility to identify key genes for a variety of biological processes.To ensure reproducibility and ease of use, our method is released as part of destiny 3.0, a popular R package for building diffusion maps from single-cell transcriptomic data. It is readily available through Bioconductor.

Published
2020

33. Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation

Author

Brunner, Andreas-David, primary, Thielert, Marvin, additional, Vasilopoulou, Catherine G., additional, Ammar, Constantin, additional, Coscia, Fabian, additional, Mund, Andreas, additional, Hoerning, Ole B., additional, Bache, Nicolai, additional, Apalategui, Amalia, additional, Lubeck, Markus, additional, Richter, Sabrina, additional, Fischer, David S., additional, Raether, Oliver, additional, Park, Melvin A., additional, Meier, Florian, additional, Theis, Fabian J., additional, and Mann, Matthias, additional

Published
2020
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35. Single-cell RNA sequencing revealsin vivosignatures of SARS-CoV-2-reactive T cells through ‘reverse phenotyping’

Author

Fischer, David S., primary, Ansari, Meshal, additional, Wagner, Karolin I., additional, Jarosch, Sebastian, additional, Huang, Yiqi, additional, Mayr, Christoph H., additional, Strunz, Maximilian, additional, Lang, Niklas J., additional, D’Ippolito, Elvira, additional, Hammel, Monika, additional, Mateyka, Laura, additional, Weber, Simone, additional, Wolff, Lisa S., additional, Witter, Klaus, additional, Fernandez, Isis E., additional, Leuschner, Gabriela, additional, Milger, Kathrin, additional, Frankenberger, Marion, additional, Nowak, Lorenz, additional, Heinig-Menhard, Katharina, additional, Koch, Ina, additional, Stoleriu, Mircea G., additional, Hilgendorff, Anne, additional, Behr, Jürgen, additional, Pichlmair, Andreas, additional, Schubert, Benjamin, additional, Theis, Fabian J., additional, Busch, Dirk H., additional, Schiller, Herbert B., additional, and Schober, Kilian, additional

Published
2020
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40. Cell-type specific impact of glucocorticoid receptor activation on the developing brain

Author

Cruceanu, Cristiana, primary, Dony, Leander, additional, Krontira, Anthi C., additional, Fischer, David S., additional, Roeh, Simone, additional, Di Giaimo, Rossella, additional, Kyrousi, Christina, additional, Arloth, Janine, additional, Czamara, Darina, additional, Martinelli, Silvia, additional, Wehner, Stefanie, additional, Breen, Michael S., additional, Koedel, Maik, additional, Sauer, Susann, additional, Rex-Haffner, Monika, additional, Cappello, Silvia, additional, Theis, Fabian J., additional, and Binder, Elisabeth B., additional

Published
2020
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46. Cancer: when to focus on palliation

Author

Carr, Daniel B., Fischer, David S., and Markman, Maurie

Subjects
Palliative treatment -- Methods, Cancer -- Care and treatment, Health, Care and treatment, Methods
Abstract

Changes in cancer therapy have prompted new consideration of the difficult question of terminating active treatment and proceeding to palliation. Treatment of many cancers has become more vigorous, for example, [...]

Published
1994

47. Cancer follow-up: how much is enough?

Author

Fischer, David S., Ungaro, Peter C., and Wilhelm, Morton C.

Subjects
Cancer patients -- Medical examination, Physicians -- Practice, Cancer -- Diagnosis, Health
Abstract

Cancer patients should undergo medical examinations at least once a year to detect a recurrence of the disease once they have undergone successful treatment. Guidelines are provided for people who suffered from cancer of the breast, cervix, colon, endometrium, lymphoma, prostate and rectum., Once the patient is free of disease, how often should you see him? Which tests are needed and when? Which aren't needed? Here are authoritative guidelines for seven common cancer [...]

Published
1992

48. Predicting antigen specificity of single T cells based on TCR CDR3 regions.

Author

Fischer, David S, Wu, Yihan, Schubert, Benjamin, and Theis, Fabian J

Subjects
*ANTIGENS, *ANTIGEN receptors, *BIG data
Abstract

It has recently become possible to simultaneously assay T‐cell specificity with respect to large sets of antigens and the T‐cell receptor sequence in high‐throughput single‐cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T‐cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single‐cell immune repertoire screens can be mitigated by modeling cell‐specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell‐to‐dextramer binding strength and receptor‐to‐pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single‐cell RNA‐seq studies on T cells without the need for MHC staining. Synopsis: TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. pMHC measurements are predicted in a large single‐cell data set with > 100,000 cells, additionally using TCR‐antigen pairs from IEDB and VDJdb.Benchmarking categorical models of antigens with antigen‐embedding models indicates that categorical models are often preferable.The study highlights the need to measure TCR specificity for a larger repertoire of antigens to generalize models to unseen antigens. [ABSTRACT FROM AUTHOR]

Published
2020
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