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1. WangLab at MEDIQA-M3G 2024: Multimodal Medical Answer Generation using Large Language Models

Author

Xie, Ronald, Palayew, Steven, Toma, Augustin, Bader, Gary, and Wang, Bo

Subjects
Computer Science - Computation and Language
Abstract

This paper outlines our submission to the MEDIQA2024 Multilingual and Multimodal Medical Answer Generation (M3G) shared task. We report results for two standalone solutions under the English category of the task, the first involving two consecutive API calls to the Claude 3 Opus API and the second involving training an image-disease label joint embedding in the style of CLIP for image classification. These two solutions scored 1st and 2nd place respectively on the competition leaderboard, substantially outperforming the next best solution. Additionally, we discuss insights gained from post-competition experiments. While the performance of these two solutions have significant room for improvement due to the difficulty of the shared task and the challenging nature of medical visual question answering in general, we identify the multi-stage LLM approach and the CLIP image classification approach as promising avenues for further investigation.

Published
2024

2. Single-cell atlas of the human brain vasculature across development, adulthood and disease

Author

Wälchli, Thomas, Ghobrial, Moheb, Schwab, Marc, Takada, Shigeki, Zhong, Hang, Suntharalingham, Samuel, Vetiska, Sandra, Gonzalez, Daymé Rodrigues, Wu, Ruilin, Rehrauer, Hubert, Dinesh, Anuroopa, Yu, Kai, Chen, Edward L. Y., Bisschop, Jeroen, Farnhammer, Fiona, Mansur, Ann, Kalucka, Joanna, Tirosh, Itay, Regli, Luca, Schaller, Karl, Frei, Karl, Ketela, Troy, Bernstein, Mark, Kongkham, Paul, Carmeliet, Peter, Valiante, Taufik, Dirks, Peter B., Suva, Mario L., Zadeh, Gelareh, Tabar, Viviane, Schlapbach, Ralph, Jackson, Hartland W., De Bock, Katrien, Fish, Jason E., Monnier, Philippe P., Bader, Gary D., and Radovanovic, Ivan

Published
2024
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4. The commitment of the human cell atlas to humanity

Author

Amit, Ido, Ardlie, Kristin, Arzuaga, Fabiana, Awandare, Gordon, Bader, Gary, Bernier, Alexander, Carninci, Piero, Donnelly, Stacey, Eils, Roland, Forrest, Alistair R. R., Greely, Henry T., Guigo, Roderic, Hacohen, Nir, Haniffa, Muzlifah, Kirby, Emily Sarah, Knoppers, Bartha Maria, Kriegstein, Arnold, Lein, Ed S., Linnarsson, Sten, Majumder, Partha P., Merad, Miriam, Meyer, Kerstin, Mhlanga, Musa M., Nolan, Garry, Ntusi, Ntobeko A. B., Pe’er, Dana, Prabhakar, Shyam, Raven-Adams, Maili, Regev, Aviv, Rozenblatt-Rosen, Orit, Saha, Senjuti, Saltzman, Andrea, Shalek, Alex K., Shin, Jay W., Stunnenberg, Henk, Teichmann, Sarah A., Tickle, Timothy, Villani, Alexandra-Chloe, Wells, Christine, Wold, Barbara, Yang, Huanming, and Zhuang, Xiaowei

Published
2024
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5. Integrated transcriptomics uncovers an enhanced association between the prion protein gene expression and vesicle dynamics signatures in glioblastomas

Author

Boccacino, Jacqueline Marcia, dos Santos Peixoto, Rafael, Fernandes, Camila Felix de Lima, Cangiano, Giovanni, Sola, Paula Rodrigues, Coelho, Bárbara Paranhos, Prado, Mariana Brandão, Melo-Escobar, Maria Isabel, de Sousa, Breno Pereira, Ayyadhury, Shamini, Bader, Gary D., Shinjo, Sueli Mieko Oba, Marie, Suely Kazue Nagahashi, da Rocha, Edroaldo Lummertz, and Lopes, Marilene Hohmuth

Published
2024
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6. The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

Author

Ma, Jun, Xie, Ronald, Ayyadhury, Shamini, Ge, Cheng, Gupta, Anubha, Gupta, Ritu, Gu, Song, Zhang, Yao, Lee, Gihun, Kim, Joonkee, Lou, Wei, Li, Haofeng, Upschulte, Eric, Dickscheid, Timo, de Almeida, José Guilherme, Wang, Yixin, Han, Lin, Yang, Xin, Labagnara, Marco, Gligorovski, Vojislav, Scheder, Maxime, Rahi, Sahand Jamal, Kempster, Carly, Pollitt, Alice, Espinosa, Leon, Mignot, Tâm, Middeke, Jan Moritz, Eckardt, Jan-Niklas, Li, Wangkai, Li, Zhaoyang, Cai, Xiaochen, Bai, Bizhe, Greenwald, Noah F., Van Valen, David, Weisbart, Erin, Cimini, Beth A., Cheung, Trevor, Brück, Oscar, Bader, Gary D., and Wang, Bo

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging., Comment: NeurIPS22 Cell Segmentation Challenge: https://neurips22-cellseg.grand-challenge.org/ . Nature Methods (2024)

Published
2023
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7. The multimodality cell segmentation challenge: toward universal solutions

Author

Ma, Jun, Xie, Ronald, Ayyadhury, Shamini, Ge, Cheng, Gupta, Anubha, Gupta, Ritu, Gu, Song, Zhang, Yao, Lee, Gihun, Kim, Joonkee, Lou, Wei, Li, Haofeng, Upschulte, Eric, Dickscheid, Timo, de Almeida, José Guilherme, Wang, Yixin, Han, Lin, Yang, Xin, Labagnara, Marco, Gligorovski, Vojislav, Scheder, Maxime, Rahi, Sahand Jamal, Kempster, Carly, Pollitt, Alice, Espinosa, Leon, Mignot, Tâm, Middeke, Jan Moritz, Eckardt, Jan-Niklas, Li, Wangkai, Li, Zhaoyang, Cai, Xiaochen, Bai, Bizhe, Greenwald, Noah F., Van Valen, David, Weisbart, Erin, Cimini, Beth A., Cheung, Trevor, Brück, Oscar, Bader, Gary D., and Wang, Bo

Published
2024
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8. Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learning

Author

Xie, Ronald, Pang, Kuan, Chung, Sai W., Perciani, Catia T., MacParland, Sonya A., Wang, Bo, and Bader, Gary D.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Histology imaging is an important tool in medical diagnosis and research, enabling the examination of tissue structure and composition at the microscopic level. Understanding the underlying molecular mechanisms of tissue architecture is critical in uncovering disease mechanisms and developing effective treatments. Gene expression profiling provides insight into the molecular processes underlying tissue architecture, but the process can be time-consuming and expensive. We present BLEEP (Bi-modaL Embedding for Expression Prediction), a bi-modal embedding framework capable of generating spatially resolved gene expression profiles of whole-slide Hematoxylin and eosin (H&E) stained histology images. BLEEP uses contrastive learning to construct a low-dimensional joint embedding space from a reference dataset using paired image and expression profiles at micrometer resolution. With this approach, the gene expression of any query image patch can be imputed using the expression profiles from the reference dataset. We demonstrate BLEEP's effectiveness in gene expression prediction by benchmarking its performance on a human liver tissue dataset captured using the 10x Visium platform, where it achieves significant improvements over existing methods. Our results demonstrate the potential of BLEEP to provide insights into the molecular mechanisms underlying tissue architecture, with important implications in diagnosis and research of various diseases. The proposed approach can significantly reduce the time and cost associated with gene expression profiling, opening up new avenues for high-throughput analysis of histology images for both research and clinical applications.

Published
2023

9. Drugst.One -- A plug-and-play solution for online systems medicine and network-based drug repurposing

Author

Maier, Andreas, Hartung, Michael, Abovsky, Mark, Adamowicz, Klaudia, Bader, Gary D., Baier, Sylvie, Blumenthal, David B., Chen, Jing, Elkjaer, Maria L., Garcia-Hernandez, Carlos, Helmy, Mohamed, Hoffmann, Markus, Jurisica, Igor, Kotlyar, Max, Lazareva, Olga, Levi, Hagai, List, Markus, Lobentanzer, Sebastian, Loscalzo, Joseph, Malod-Dognin, Noel, Manz, Quirin, Matschinske, Julian, Mee, Miles, Oubounyt, Mhaned, Pico, Alexander R., Pillich, Rudolf T., Poschenrieder, Julian M., Pratt, Dexter, Pržulj, Nataša, Sadegh, Sepideh, Saez-Rodriguez, Julio, Sarkar, Suryadipto, Shaked, Gideon, Shamir, Ron, Trummer, Nico, Turhan, Ugur, Wang, Ruisheng, Zolotareva, Olga, and Baumbach, Jan

Subjects
Quantitative Biology - Quantitative Methods
Abstract

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research., Comment: 45 pages, 6 figures, 7 tables

Published
2023

11. Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

Author

Diaz-Mejia, J Javier, Meng, Elaine C, Pico, Alexander R, MacParland, Sonya A, Ketela, Troy, Pugh, Trevor J, Bader, Gary D, and Morris, John H

Subjects
Networking and Information Technology R&D (NITRD), Genetics, Prevention, Good Health and Well Being, Biochemistry and Cell Biology, Clinical Sciences, Oncology and Carcinogenesis
Abstract

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated computational steps like data normalization, dimensionality reduction and cell clustering. However, assigning cell type labels to cell clusters is still conducted manually by most researchers, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. Two bottlenecks to automating this task are the scarcity of reference cell type gene expression signatures and the fact that some dedicated methods are available only as web servers with limited cell type gene expression signatures. Methods: In this study, we benchmarked four methods (CIBERSORT, GSEA, GSVA, and ORA) for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used scRNA-seq datasets from liver, peripheral blood mononuclear cells and retinal neurons for which reference cell type gene expression signatures were available. Results: Our results show that, in general, all four methods show a high performance in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.94, sd = 0.036), whereas precision-recall curve analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). Conclusions: CIBERSORT and GSVA were the top two performers. Additionally, GSVA was the fastest of the four methods and was more robust in cell type gene expression signature subsampling simulations. We provide an extensible framework to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

Published
2023

12. Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval

Author

Giorgi, John, Soldaini, Luca, Wang, Bo, Bader, Gary, Lo, Kyle, Wang, Lucy Lu, and Cohan, Arman

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Multi-document summarization (MDS) assumes a set of topic-related documents are provided as input. In practice, this document set is not always available; it would need to be retrieved given an information need, i.e. a question or topic statement, a setting we dub "open-domain" MDS. We study this more challenging setting by formalizing the task and bootstrapping it using existing datasets, retrievers and summarizers. Via extensive automatic and human evaluation, we determine: (1) state-of-the-art summarizers suffer large reductions in performance when applied to open-domain MDS, (2) additional training in the open-domain setting can reduce this sensitivity to imperfect retrieval, and (3) summarizers are insensitive to the retrieval of duplicate documents and the order of retrieved documents, but highly sensitive to other errors, like the retrieval of irrelevant documents. Based on our results, we provide practical guidelines to enable future work on open-domain MDS, e.g. how to choose the number of retrieved documents to summarize. Our results suggest that new retrieval and summarization methods and annotated resources for training and evaluation are necessary for further progress in the open-domain setting., Comment: Accepted to EMNLP Findings 2023

Published
2022

13. A sequence-to-sequence approach for document-level relation extraction

Author

Giorgi, John, Bader, Gary D., and Wang, Bo

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Motivated by the fact that many relations cross the sentence boundary, there has been increasing interest in document-level relation extraction (DocRE). DocRE requires integrating information within and across sentences, capturing complex interactions between mentions of entities. Most existing methods are pipeline-based, requiring entities as input. However, jointly learning to extract entities and relations can improve performance and be more efficient due to shared parameters and training steps. In this paper, we develop a sequence-to-sequence approach, seq2rel, that can learn the subtasks of DocRE (entity extraction, coreference resolution and relation extraction) end-to-end, replacing a pipeline of task-specific components. Using a simple strategy we call entity hinting, we compare our approach to existing pipeline-based methods on several popular biomedical datasets, in some cases exceeding their performance. We also report the first end-to-end results on these datasets for future comparison. Finally, we demonstrate that, under our model, an end-to-end approach outperforms a pipeline-based approach. Our code, data and trained models are available at {\url{https://github.com/johngiorgi/seq2rel}}. An online demo is available at {\url{https://share.streamlit.io/johngiorgi/seq2rel/main/demo.py}}., Comment: Camera-ready copy for BioNLP 2022 @ ACL 2022

Published
2022

15. Single-cell, single-nucleus, and spatial transcriptomics characterization of the immunological landscape in the healthy and PSC human liver

Author

Andrews, Tallulah S., Nakib, Diana, Perciani, Catia T., Ma, Xue Zhong, Liu, Lewis, Winter, Erin, Camat, Damra, Chung, Sai W., Lumanto, Patricia, Manuel, Justin, Mangroo, Shantel, Hansen, Bettina, Arpinder, Bal, Thoeni, Cornelia, Sayed, Blayne, Feld, Jordan, Gehring, Adam, Gulamhusein, Aliya, Hirschfield, Gideon M., Ricciuto, Amanda, Bader, Gary D., McGilvray, Ian D., and MacParland, Sonya

Published
2024
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16. Identification of the global miR-130a targetome reveals a role for TBL1XR1 in hematopoietic stem cell self-renewal and t(8;21) AML

Author

Krivdova, Gabriela, Voisin, Veronique, Schoof, Erwin M, Marhon, Sajid A, Murison, Alex, McLeod, Jessica L, Gabra, Martino M, Zeng, Andy GX, Aigner, Stefan, Yee, Brian A, Shishkin, Alexander A, Van Nostrand, Eric L, Hermans, Karin G, Trotman-Grant, Aaron C, Mbong, Nathan, Kennedy, James A, Gan, Olga I, Wagenblast, Elvin, De Carvalho, Daniel D, Salmena, Leonardo, Minden, Mark D, Bader, Gary D, Yeo, Gene W, Dick, John E, and Lechman, Eric R

Subjects
Biological Sciences, Stem Cell Research - Nonembryonic - Human, Cancer, Stem Cell Research, Hematology, Pediatric, Rare Diseases, Pediatric Cancer, Biotechnology, Regenerative Medicine, Genetics, Childhood Leukemia, Stem Cell Research - Nonembryonic - Non-Human, Underpinning research, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Aetiology, Cell Line, Tumor, Cell Self Renewal, Hematopoietic Stem Cells, Humans, Leukemia, Myeloid, Acute, MicroRNAs, Receptors, Cytoplasmic and Nuclear, Repressor Proteins, 21) AML, AML1-ETO, TBL1XR1, acute myeloid leukemia, chimeric AGO2 eCLIP-seq, hematopoietic stem cell, microRNA, molecular network, self-renewal, stemness, t(8, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

Gene expression profiling and proteome analysis of normal and malignant hematopoietic stem cells (HSCs) point to shared core stemness properties. However, discordance between mRNA and protein signatures highlights an important role for post-transcriptional regulation by microRNAs (miRNAs) in governing this critical nexus. Here, we identify miR-130a as a regulator of HSC self-renewal and differentiation. Enforced expression of miR-130a impairs B lymphoid differentiation and expands long-term HSCs. Integration of protein mass spectrometry and chimeric AGO2 crosslinking and immunoprecipitation (CLIP) identifies TBL1XR1 as a primary miR-130a target, whose loss of function phenocopies miR-130a overexpression. Moreover, we report that miR-130a is highly expressed in t(8;21) acute myeloid leukemia (AML), where it is critical for maintaining the oncogenic molecular program mediated by the AML1-ETO complex. Our study establishes that identification of the comprehensive miRNA targetome within primary cells enables discovery of genes and molecular networks underpinning stemness properties of normal and leukemic cells.

Published
2022

17. DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations

Author

Giorgi, John, Nitski, Osvald, Wang, Bo, and Bader, Gary

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Sentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various downstream tasks, such as clustering and retrieval. Unlike word embeddings, the highest performing solutions for learning sentence embeddings require labelled data, limiting their usefulness to languages and domains where labelled data is abundant. In this paper, we present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations. Inspired by recent advances in deep metric learning (DML), we carefully design a self-supervised objective for learning universal sentence embeddings that does not require labelled training data. When used to extend the pretraining of transformer-based language models, our approach closes the performance gap between unsupervised and supervised pretraining for universal sentence encoders. Importantly, our experiments suggest that the quality of the learned embeddings scale with both the number of trainable parameters and the amount of unlabelled training data. Our code and pretrained models are publicly available and can be easily adapted to new domains or used to embed unseen text., Comment: ACL2021 Camera Ready V2

Published
2020

18. Distinct shared and compartment-enriched oncogenic networks drive primary versus metastatic breast cancer

Author

Jiang, Zhe, Ju, YoungJun, Ali, Amjad, Chung, Philip E. D., Skowron, Patryk, Wang, Dong-Yu, Shrestha, Mariusz, Li, Huiqin, Liu, Jeff C., Vorobieva, Ioulia, Ghanbari-Azarnier, Ronak, Mwewa, Ethel, Koritzinsky, Marianne, Ben-David, Yaacov, Woodgett, James R., Perou, Charles M., Dupuy, Adam, Bader, Gary D., Egan, Sean E., Taylor, Michael D., and Zacksenhaus, Eldad

Published
2023
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19. Multiplatform molecular profiling uncovers two subgroups of malignant peripheral nerve sheath tumors with distinct therapeutic vulnerabilities

Author

Suppiah, Suganth, Mansouri, Sheila, Mamatjan, Yasin, Liu, Jeffrey C., Bhunia, Minu M., Patil, Vikas, Rath, Prisni, Mehani, Bharati, Heir, Pardeep, Bunda, Severa, Velez-Reyes, German L., Singh, Olivia, Ijad, Nazanin, Pirouzmand, Neda, Dalcourt, Tatyana, Meng, Ying, Karimi, Shirin, Wei, Qingxia, Nassiri, Farshad, Pugh, Trevor J., Bader, Gary D., Aldape, Kenneth D., Largaespada, David A., and Zadeh, Gelareh

Published
2023
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20. A roadmap for the Human Developmental Cell Atlas

Author

Haniffa, Muzlifah, Taylor, Deanne, Linnarsson, Sten, Aronow, Bruce J, Bader, Gary D, Barker, Roger A, Camara, Pablo G, Camp, J Gray, Chédotal, Alain, Copp, Andrew, Etchevers, Heather C, Giacobini, Paolo, Göttgens, Berthold, Guo, Guoji, Hupalowska, Ania, James, Kylie R, Kirby, Emily, Kriegstein, Arnold, Lundeberg, Joakim, Marioni, John C, Meyer, Kerstin B, Niakan, Kathy K, Nilsson, Mats, Olabi, Bayanne, Pe’er, Dana, Regev, Aviv, Rood, Jennifer, Rozenblatt-Rosen, Orit, Satija, Rahul, Teichmann, Sarah A, Treutlein, Barbara, Vento-Tormo, Roser, and Webb, Simone

Subjects
Medical Biotechnology, Biomedical and Clinical Sciences, Regenerative Medicine, Stem Cell Research - Nonembryonic - Human, Stem Cell Research, Pediatric, Stem Cell Research - Embryonic - Human, Human Fetal Tissue, Genetics, Human Genome, Good Health and Well Being, Adult, Animals, Atlases as Topic, Cell Culture Techniques, Cell Movement, Cell Survival, Cell Tracking, Cells, Data Visualization, Developmental Biology, Embryo, Mammalian, Female, Fetus, Humans, Imaging, Three-Dimensional, Information Dissemination, Male, Models, Animal, Organogenesis, Organoids, Stem Cells, Human Cell Atlas Developmental Biological Network, General Science & Technology
Abstract

The Human Developmental Cell Atlas (HDCA) initiative, which is part of the Human Cell Atlas, aims to create a comprehensive reference map of cells during development. This will be critical to understanding normal organogenesis, the effect of mutations, environmental factors and infectious agents on human development, congenital and childhood disorders, and the cellular basis of ageing, cancer and regenerative medicine. Here we outline the HDCA initiative and the challenges of mapping and modelling human development using state-of-the-art technologies to create a reference atlas across gestation. Similar to the Human Genome Project, the HDCA will integrate the output from a growing community of scientists who are mapping human development into a unified atlas. We describe the early milestones that have been achieved and the use of human stem-cell-derived cultures, organoids and animal models to inform the HDCA, especially for prenatal tissues that are hard to acquire. Finally, we provide a roadmap towards a complete atlas of human development.

Published
2021

21. A clinically applicable integrative molecular classification of meningiomas

Author

Nassiri, Farshad, Liu, Jeff, Patil, Vikas, Mamatjan, Yasin, Wang, Justin Z, Hugh-White, Rupert, Macklin, Andrew M, Khan, Shahbaz, Singh, Olivia, Karimi, Shirin, Corona, Rosario I, Liu, Lydia Y, Chen, Caroline Y, Chakravarthy, Ankur, Wei, Qingxia, Mehani, Bharati, Suppiah, Suganth, Gao, Andrew, Workewych, Adriana M, Tabatabai, Ghazaleh, Boutros, Paul C, Bader, Gary D, de Carvalho, Daniel D, Kislinger, Thomas, Aldape, Kenneth, and Zadeh, Gelareh

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Clinical Sciences, Oncology and Carcinogenesis, Brain Cancer, Rare Diseases, Brain Disorders, Cancer, Human Genome, Biomarkers, Tumor, DNA Methylation, Data Analysis, Drug Discovery, Female, Gene Expression Regulation, Neoplastic, Humans, Immunohistochemistry, Male, Meningioma, Mutation, Proteogenomics, RNA-Seq, Reproducibility of Results, Single-Cell Analysis, General Science & Technology
Abstract

Meningiomas are the most common primary intracranial tumour in adults1. Patients with symptoms are generally treated with surgery as there are no effective medical therapies. The World Health Organization histopathological grade of the tumour and the extent of resection at surgery (Simpson grade) are associated with the recurrence of disease; however, they do not accurately reflect the clinical behaviour of all meningiomas2. Molecular classifications of meningioma that reliably reflect tumour behaviour and inform on therapies are required. Here we introduce four consensus molecular groups of meningioma by combining DNA somatic copy-number aberrations, DNA somatic point mutations, DNA methylation and messenger RNA abundance in a unified analysis. These molecular groups more accurately predicted clinical outcomes compared with existing classification schemes. Each molecular group showed distinctive and prototypical biology (immunogenic, benign NF2 wild-type, hypermetabolic and proliferative) that informed therapeutic options. Proteogenomic characterization reinforced the robustness of the newly defined molecular groups and uncovered highly abundant and group-specific protein targets that we validated using immunohistochemistry. Single-cell RNA sequencing revealed inter-individual variations in meningioma as well as variations in intrinsic expression programs in neoplastic cells that mirrored the biology of the molecular groups identified.

Published
2021

23. The transcriptional landscape of Shh medulloblastoma.

Author

Skowron, Patryk, Farooq, Hamza, Cavalli, Florence MG, Morrissy, A Sorana, Ly, Michelle, Hendrikse, Liam D, Wang, Evan Y, Djambazian, Haig, Zhu, Helen, Mungall, Karen L, Trinh, Quang M, Zheng, Tina, Dai, Shizhong, Stucklin, Ana S Guerreiro, Vladoiu, Maria C, Fong, Vernon, Holgado, Borja L, Nor, Carolina, Wu, Xiaochong, Abd-Rabbo, Diala, Bérubé, Pierre, Wang, Yu Chang, Luu, Betty, Suarez, Raul A, Rastan, Avesta, Gillmor, Aaron H, Lee, John JY, Zhang, Xiao Yun, Daniels, Craig, Dirks, Peter, Malkin, David, Bouffet, Eric, Tabori, Uri, Loukides, James, Doz, François P, Bourdeaut, Franck, Delattre, Olivier O, Masliah-Planchon, Julien, Ayrault, Olivier, Kim, Seung-Ki, Meyronet, David, Grajkowska, Wieslawa A, Carlotti, Carlos G, de Torres, Carmen, Mora, Jaume, Eberhart, Charles G, Van Meir, Erwin G, Kumabe, Toshihiro, French, Pim J, Kros, Johan M, Jabado, Nada, Lach, Boleslaw, Pollack, Ian F, Hamilton, Ronald L, Rao, Amulya A Nageswara, Giannini, Caterina, Olson, James M, Bognár, László, Klekner, Almos, Zitterbart, Karel, Phillips, Joanna J, Thompson, Reid C, Cooper, Michael K, Rubin, Joshua B, Liau, Linda M, Garami, Miklós, Hauser, Peter, Li, Kay Ka Wai, Ng, Ho-Keung, Poon, Wai Sang, Yancey Gillespie, G, Chan, Jennifer A, Jung, Shin, McLendon, Roger E, Thompson, Eric M, Zagzag, David, Vibhakar, Rajeev, Ra, Young Shin, Garre, Maria Luisa, Schüller, Ulrich, Shofuda, Tomoko, Faria, Claudia C, López-Aguilar, Enrique, Zadeh, Gelareh, Hui, Chi-Chung, Ramaswamy, Vijay, Bailey, Swneke D, Jones, Steven J, Mungall, Andrew J, Moore, Richard A, Calarco, John A, Stein, Lincoln D, Bader, Gary D, Reimand, Jüri, Ragoussis, Jiannis, Weiss, William A, Marra, Marco A, Suzuki, Hiromichi, and Taylor, Michael D

Subjects
Humans, Medulloblastoma, Cerebellar Neoplasms, Signal Transduction, Gene Expression Regulation, Neoplastic, Adolescent, Adult, Middle Aged, Child, Child, Preschool, Infant, Female, Male, Hedgehog Proteins, Gene Regulatory Networks, Genetic Variation, Young Adult, Transcriptome, Pediatric Research Initiative, Brain Cancer, Pediatric, Rare Diseases, Genetics, Pediatric Cancer, Clinical Research, Brain Disorders, Neurosciences, Biotechnology, Human Genome, Cancer, 2.1 Biological and endogenous factors
Abstract

Sonic hedgehog medulloblastoma encompasses a clinically and molecularly diverse group of cancers of the developing central nervous system. Here, we use unbiased sequencing of the transcriptome across a large cohort of 250 tumors to reveal differences among molecular subtypes of the disease, and demonstrate the previously unappreciated importance of non-coding RNA transcripts. We identify alterations within the cAMP dependent pathway (GNAS, PRKAR1A) which converge on GLI2 activity and show that 18% of tumors have a genetic event that directly targets the abundance and/or stability of MYCN. Furthermore, we discover an extensive network of fusions in focally amplified regions encompassing GLI2, and several loss-of-function fusions in tumor suppressor genes PTCH1, SUFU and NCOR1. Molecular convergence on a subset of genes by nucleotide variants, copy number aberrations, and gene fusions highlight the key roles of specific pathways in the pathogenesis of Sonic hedgehog medulloblastoma and open up opportunities for therapeutic intervention.

Published
2021

24. scNetViz: from single cells to networks using Cytoscape.

Author

Choudhary, Krishna, Meng, Elaine C, Diaz-Mejia, J Javier, Bader, Gary D, Pico, Alexander R, and Morris, John H

Subjects
Automation, Software, Gene Regulatory Networks, Workflow, Data Analysis, App, Cytoscape, Expression analysis, Network biology, Single cell, scRNA-seq, Genetics, Human Genome, Biotechnology, Underpinning research, 1.1 Normal biological development and functioning, Generic health relevance, Biochemistry and Cell Biology, Clinical Sciences, Oncology and Carcinogenesis
Abstract

Single-cell RNA-sequencing (scRNA-seq) has revolutionized molecular biology and medicine by enabling high-throughput studies of cellular heterogeneity in diverse tissues. Applying network biology approaches to scRNA-seq data can provide useful insights into genes driving heterogeneous cell-type compositions of tissues. Here, we present scNetViz-  a Cytoscape app to aid biological interpretation of cell clusters in scRNA-seq data using network analysis. scNetViz calculates the differential expression of each gene across clusters and then creates a cluster-specific gene functional interaction network between the significantly differentially expressed genes for further analysis, such as pathway enrichment analysis. To automate a complete data analysis workflow, scNetViz integrates parts of the Scanpy software, which is a popular Python package for scRNA-seq data analysis, with Cytoscape apps such as stringApp, cyPlot, and enhancedGraphics. We describe our implementation of methods for accessing data from public single cell atlas projects, differential expression analysis, visualization, and automation. scNetViz enables users to analyze data from public atlases or their own experiments, which we illustrate with two use cases. Analysis can be performed via the Cytoscape GUI or CyREST programming interface using R (RCy3) or Python (py4cytoscape).

Published
2021

25. End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models

Author

Giorgi, John, Wang, Xindi, Sahar, Nicola, Shin, Won Young, Bader, Gary D., and Wang, Bo

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

Named entity recognition (NER) and relation extraction (RE) are two important tasks in information extraction and retrieval (IE \& IR). Recent work has demonstrated that it is beneficial to learn these tasks jointly, which avoids the propagation of error inherent in pipeline-based systems and improves performance. However, state-of-the-art joint models typically rely on external natural language processing (NLP) tools, such as dependency parsers, limiting their usefulness to domains (e.g. news) where those tools perform well. The few neural, end-to-end models that have been proposed are trained almost completely from scratch. In this paper, we propose a neural, end-to-end model for jointly extracting entities and their relations which does not rely on external NLP tools and which integrates a large, pre-trained language model. Because the bulk of our model's parameters are pre-trained and we eschew recurrence for self-attention, our model is fast to train. On 5 datasets across 3 domains, our model matches or exceeds state-of-the-art performance, sometimes by a large margin., Comment: 12 pages, 2 figures

Published
2019

26. SBML Level 3: an extensible format for the exchange and reuse of biological models

Author

Keating, Sarah M, Waltemath, Dagmar, König, Matthias, Zhang, Fengkai, Dräger, Andreas, Chaouiya, Claudine, Bergmann, Frank T, Finney, Andrew, Gillespie, Colin S, Helikar, Tomáš, Hoops, Stefan, Malik‐Sheriff, Rahuman S, Moodie, Stuart L, Moraru, Ion I, Myers, Chris J, Naldi, Aurélien, Olivier, Brett G, Sahle, Sven, Schaff, James C, Smith, Lucian P, Swat, Maciej J, Thieffry, Denis, Watanabe, Leandro, Wilkinson, Darren J, Blinov, Michael L, Begley, Kimberly, Faeder, James R, Gómez, Harold F, Hamm, Thomas M, Inagaki, Yuichiro, Liebermeister, Wolfram, Lister, Allyson L, Lucio, Daniel, Mjolsness, Eric, Proctor, Carole J, Raman, Karthik, Rodriguez, Nicolas, Shaffer, Clifford A, Shapiro, Bruce E, Stelling, Joerg, Swainston, Neil, Tanimura, Naoki, Wagner, John, Meier‐Schellersheim, Martin, Sauro, Herbert M, Palsson, Bernhard, Bolouri, Hamid, Kitano, Hiroaki, Funahashi, Akira, Hermjakob, Henning, Doyle, John C, Hucka, Michael, Adams, Richard R, Allen, Nicholas A, Angermann, Bastian R, Antoniotti, Marco, Bader, Gary D, Červený, Jan, Courtot, Mélanie, Cox, Chris D, Pezze, Piero Dalle, Demir, Emek, Denney, William S, Dharuri, Harish, Dorier, Julien, Drasdo, Dirk, Ebrahim, Ali, Eichner, Johannes, Elf, Johan, Endler, Lukas, Evelo, Chris T, Flamm, Christoph, Fleming, Ronan MT, Fröhlich, Martina, Glont, Mihai, Gonçalves, Emanuel, Golebiewski, Martin, Grabski, Hovakim, Gutteridge, Alex, Hachmeister, Damon, Harris, Leonard A, Heavner, Benjamin D, Henkel, Ron, Hlavacek, William S, Hu, Bin, Hyduke, Daniel R, de Jong, Hidde, Juty, Nick, Karp, Peter D, Karr, Jonathan R, Kell, Douglas B, Keller, Roland, Kiselev, Ilya, Klamt, Steffen, Klipp, Edda, Knüpfer, Christian, Kolpakov, Fedor, Krause, Falko, Kutmon, Martina, and Laibe, Camille

Subjects
Bioengineering, Networking and Information Technology R&D (NITRD), Animals, Humans, Logistic Models, Models, Biological, Software, Systems Biology, computational modeling, file format, interoperability, reproducibility, systems biology, SBML Level 3 Community members, Biochemistry and Cell Biology, Other Biological Sciences, Bioinformatics
Abstract

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

Published
2020

27. Single-cell atlas of human liver development reveals pathways directing hepatic cell fates

Author

Wesley, Brandon T., Ross, Alexander D. B., Muraro, Daniele, Miao, Zhichao, Saxton, Sarah, Tomaz, Rute A., Morell, Carola M., Ridley, Katherine, Zacharis, Ekaterini D., Petrus-Reurer, Sandra, Kraiczy, Judith, Mahbubani, Krishnaa T., Brown, Stephanie, Garcia-Bernardo, Jose, Alsinet, Clara, Gaffney, Daniel, Horsfall, Dave, Tysoe, Olivia C., Botting, Rachel A., Stephenson, Emily, Popescu, Dorin-Mirel, MacParland, Sonya, Bader, Gary, McGilvray, Ian D., Ortmann, Daniel, Sampaziotis, Fotios, Saeb-Parsy, Kourosh, Haniffa, Muzlifah, Stevens, Kelly R., Zilbauer, Matthias, Teichmann, Sarah A., and Vallier, Ludovic

Published
2022
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29. The Human Cell Atlas White Paper

Author

Regev, Aviv, Teichmann, Sarah, Rozenblatt-Rosen, Orit, Stubbington, Michael, Ardlie, Kristin, Amit, Ido, Arlotta, Paola, Bader, Gary, Benoist, Christophe, Biton, Moshe, Bodenmiller, Bernd, Bruneau, Benoit, Campbell, Peter, Carmichael, Mary, Carninci, Piero, Castelo-Soccio, Leslie, Clatworthy, Menna, Clevers, Hans, Conrad, Christian, Eils, Roland, Freeman, Jeremy, Fugger, Lars, Goettgens, Berthold, Graham, Daniel, Greka, Anna, Hacohen, Nir, Haniffa, Muzlifah, Helbig, Ingo, Heuckeroth, Robert, Kathiresan, Sekar, Kim, Seung, Klein, Allon, Knoppers, Bartha, Kriegstein, Arnold, Lander, Eric, Lee, Jane, Lein, Ed, Linnarsson, Sten, Macosko, Evan, MacParland, Sonya, Majovski, Robert, Majumder, Partha, Marioni, John, McGilvray, Ian, Merad, Miriam, Mhlanga, Musa, Naik, Shalin, Nawijn, Martijn, Nolan, Garry, Paten, Benedict, Pe'er, Dana, Philippakis, Anthony, Ponting, Chris, Quake, Steve, Rajagopal, Jayaraj, Rajewsky, Nikolaus, Reik, Wolf, Rood, Jennifer, Saeb-Parsy, Kourosh, Schiller, Herbert, Scott, Steve, Shalek, Alex, Shapiro, Ehud, Shin, Jay, Skeldon, Kenneth, Stratton, Michael, Streicher, Jenna, Stunnenberg, Henk, Tan, Kai, Taylor, Deanne, Thorogood, Adrian, Vallier, Ludovic, van Oudenaarden, Alexander, Watt, Fiona, Weicher, Wilko, Weissman, Jonathan, Wells, Andrew, Wold, Barbara, Xavier, Ramnik, Zhuang, Xiaowei, and Committee, Human Cell Atlas Organizing

Subjects
Quantitative Biology - Tissues and Organs
Abstract

The Human Cell Atlas (HCA) will be made up of comprehensive reference maps of all human cells - the fundamental units of life - as a basis for understanding fundamental human biological processes and diagnosing, monitoring, and treating disease. It will help scientists understand how genetic variants impact disease risk, define drug toxicities, discover better therapies, and advance regenerative medicine. A resource of such ambition and scale should be built in stages, increasing in size, breadth, and resolution as technologies develop and understanding deepens. We will therefore pursue Phase 1 as a suite of flagship projects in key tissues, systems, and organs. We will bring together experts in biology, medicine, genomics, technology development and computation (including data analysis, software engineering, and visualization). We will also need standardized experimental and computational methods that will allow us to compare diverse cell and tissue types - and samples across human communities - in consistent ways, ensuring that the resulting resource is truly global. This document, the first version of the HCA White Paper, was written by experts in the field with feedback and suggestions from the HCA community, gathered during recent international meetings. The White Paper, released at the close of this yearlong planning process, will be a living document that evolves as the HCA community provides additional feedback, as technological and computational advances are made, and as lessons are learned during the construction of the atlas.

Published
2018

30. Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data.

Author

Diaz-Mejia, J Javier, Meng, Elaine C, Pico, Alexander R, MacParland, Sonya A, Ketela, Troy, Pugh, Trevor J, Bader, Gary D, and Morris, John H

Subjects
Leukocytes, Mononuclear, Animals, Humans, Mice, RNA, Reproducibility of Results, Gene Expression Profiling, Algorithms, Single-Cell Analysis, RNA-seq, benchmark, bioinformatics, cell type, evaluation, labeling, scRNA-seq, single cell, Genetics, Networking and Information Technology R&D (NITRD), Good Health and Well Being, Biochemistry and Cell Biology, Clinical Sciences, Oncology and Carcinogenesis
Abstract

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated steps from normalization to cell clustering. However, assigning cell type labels to cell clusters is often conducted manually, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. This is partially due to the scarcity of reference cell type signatures and because some methods support limited cell type signatures. Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used five scRNA-seq datasets: human liver, 11 Tabula Muris mouse tissues, two human peripheral blood mononuclear cell datasets, and mouse retinal neurons, for which reference cell type signatures were available. The datasets span Drop-seq, 10X Chromium and Seq-Well technologies and range in size from ~3,700 to ~68,000 cells. Results: Our results show that, in general, all five methods perform well in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.91, sd = 0.06), whereas precision-recall analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). We observed an influence of the number of genes in cell type signatures on performance, with smaller signatures leading more frequently to incorrect results. Conclusions: GSVA was the overall top performer and was more robust in cell type signature subsampling simulations, although different methods performed well using different datasets. METANEIGHBOR and GSVA were the fastest methods. CIBERSORT and METANEIGHBOR were more influenced than the other methods by analyses including only expected cell types. We provide an extensible framework that can be used to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

Published
2019

31. Neuron-specific protein network mapping of autism risk genes identifies shared biological mechanisms and disease-relevant pathologies

Author

Murtaza, Nadeem, Cheng, Annie A., Brown, Chad O., Meka, Durga Praveen, Hong, Shuai, Uy, Jarryll A., El-Hajjar, Joelle, Pipko, Neta, Unda, Brianna K., Schwanke, Birgit, Xing, Sansi, Thiruvahindrapuram, Bhooma, Engchuan, Worrawat, Trost, Brett, Deneault, Eric, Calderon de Anda, Froylan, Doble, Bradley W., Ellis, James, Anagnostou, Evdokia, Bader, Gary D., Scherer, Stephen W., Lu, Yu, and Singh, Karun K.

Published
2022
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34. The metabolic enzyme hexokinase 2 localizes to the nucleus in AML and normal haematopoietic stem and progenitor cells to maintain stemness

Author

Thomas, Geethu Emily, Egan, Grace, García-Prat, Laura, Botham, Aaron, Voisin, Veronique, Patel, Parasvi S., Hoff, Fieke W., Chin, Jordan, Nachmias, Boaz, Kaufmann, Kerstin B., Khan, Dilshad H., Hurren, Rose, Wang, Xiaoming, Gronda, Marcela, MacLean, Neil, O’Brien, Cristiana, Singh, Rashim P., Jones, Courtney L., Harding, Shane M., Raught, Brian, Arruda, Andrea, Minden, Mark D., Bader, Gary D., Hakem, Razq, Kornblau, Steve, Dick, John E., and Schimmer, Aaron D.

Published
2022
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35. IPO11 regulates the nuclear import of BZW1/2 and is necessary for AML cells and stem cells

Author

Nachmias, Boaz, Khan, Dilshad H., Voisin, Veronique, Mer, Arvind S., Thomas, Geethu Emily, Segev, Nadav, St-Germain, Jonathan, Hurren, Rose, Gronda, Marcela, Botham, Aaron, Wang, Xiaoming, Maclean, Neil, Seneviratne, Ayesh K., Duong, Nathan, Xu, Changjiang, Arruda, Andrea, Orouji, Elias, Algouneh, Arash, Hakem, Razqallah, Shlush, Liran, Minden, Mark D., Raught, Brian, Bader, Gary D., and Schimmer, Aaron D.

Published
2022
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36. Mammary molecular portraits reveal lineage-specific features and progenitor cell vulnerabilities

Author

Casey, Alison E, Sinha, Ankit, Singhania, Rajat, Livingstone, Julie, Waterhouse, Paul, Tharmapalan, Pirashaanthy, Cruickshank, Jennifer, Shehata, Mona, Drysdale, Erik, Fang, Hui, Kim, Hyeyeon, Isserlin, Ruth, Bailey, Swneke, Medina, Tiago, Deblois, Genevieve, Shiah, Yu-Jia, Barsyte-Lovejoy, Dalia, Hofer, Stefan, Bader, Gary, Lupien, Mathieu, Arrowsmith, Cheryl, Knapp, Stefan, De Carvalho, Daniel, Berman, Hal, Boutros, Paul C, Kislinger, Thomas, and Khokha, Rama

Subjects
Prevention, Stem Cell Research - Nonembryonic - Non-Human, Cancer, Biotechnology, Human Genome, Stem Cell Research - Nonembryonic - Human, Breast Cancer, Stem Cell Research, Regenerative Medicine, Genetics, 1.1 Normal biological development and functioning, Underpinning research, Generic health relevance, Animals, Breast Neoplasms, Cell Line, Tumor, Cell Lineage, DNA Methylation, DNA, Neoplasm, Epigenesis, Genetic, Epigenomics, Humans, Mice, Mice, Transgenic, Neoplastic Stem Cells, Progesterone, Proteome, RNA, Neoplasm, Risk Factors, Transcriptome, Tumor Suppressor Protein p53, Up-Regulation, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

The mammary epithelium depends on specific lineages and their stem and progenitor function to accommodate hormone-triggered physiological demands in the adult female. Perturbations of these lineages underpin breast cancer risk, yet our understanding of normal mammary cell composition is incomplete. Here, we build a multimodal resource for the adult gland through comprehensive profiling of primary cell epigenomes, transcriptomes, and proteomes. We define systems-level relationships between chromatin-DNA-RNA-protein states, identify lineage-specific DNA methylation of transcription factor binding sites, and pinpoint proteins underlying progesterone responsiveness. Comparative proteomics of estrogen and progesterone receptor-positive and -negative cell populations, extensive target validation, and drug testing lead to discovery of stem and progenitor cell vulnerabilities. Top epigenetic drugs exert cytostatic effects; prevent adult mammary cell expansion, clonogenicity, and mammopoiesis; and deplete stem cell frequency. Select drugs also abrogate human breast progenitor cell activity in normal and high-risk patient samples. This integrative computational and functional study provides fundamental insight into mammary lineage and stem cell biology.

Published
2018

37. The Cytoscape Automation app article collection

Author

Demchak, Barry, Otasek, David, Pico, Alexander R, Bader, Gary D, Ono, Keiichiro, Settle, Brett, Sage, Eric, Morris, John H, Longabaugh, William, Lopes, Christian, Kucera, Michael, Treister, Adam, Schwikowski, Benno, Molenaar, Piet, and Ideker, Trey

Subjects
Clinical Research, Networking and Information Technology R&D (NITRD), App, Automation, Cytoscape, Network Analysis, Network Biology, Network Visualization, Biochemistry and Cell Biology, Clinical Sciences, Oncology and Carcinogenesis
Abstract

Cytoscape is the premiere platform for interactive analysis, integration and visualization of network data. While Cytoscape itself delivers much basic functionality, it relies on community-written apps to deliver specialized functions and analyses. To date, Cytoscape's CyREST feature has allowed researchers to write workflows that call basic Cytoscape functions, but provides no access to its high value app-based functions. With Cytoscape Automation, workflows can now call apps that have been upgraded to expose their functionality. This article collection is a resource to assist readers in quickly and economically leveraging such apps in reproducible workflows that scale independently to large data sets and production runs.

Published
2018

38. Single-cell profiling of healthy human kidney reveals features of sex-based transcriptional programs and tissue-specific immunity

Author

McEvoy, Caitriona M., Murphy, Julia M., Zhang, Lin, Clotet-Freixas, Sergi, Mathews, Jessica A., An, James, Karimzadeh, Mehran, Pouyabahar, Delaram, Su, Shenghui, Zaslaver, Olga, Röst, Hannes, Arambewela, Rangi, Liu, Lewis Y., Zhang, Sally, Lawson, Keith A., Finelli, Antonio, Wang, Bo, MacParland, Sonya A., Bader, Gary D., Konvalinka, Ana, and Crome, Sarah Q.

Published
2022
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39. OS-040-YI Claudin-1 is a mediator and therapeutic target of biliary fibrosis by modulating liver progenitor cell fate and signaling

Author

Zompo, Fabio Del, primary, Crouchet, Emilie, additional, Ostyn, Tessa, additional, Jühling, Frank, additional, Moehlin, Julien, additional, Roehlen, Natascha, additional, Andrews, Tallulah, additional, Nakib, Diana, additional, Perciani, Catia, additional, Chung, Sai, additional, Bader, Gary L., additional, Mcgilvray, Ian, additional, MacParland, Sonya, additional, Iacone, Roberto, additional, Teixeira, Geoffrey, additional, Heikenwälder, Mathias, additional, Govaere, Olivier, additional, Roskams, Tania, additional, Schuster, Catherine, additional, Mailly, Laurent, additional, and Baumert, Thomas, additional

Published
2024
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40. Drugst.One — a plug-and-play solution for online systems medicine and network-based drug repurposing

Author

Maier, Andreas, primary, Hartung, Michael, additional, Abovsky, Mark, additional, Adamowicz, Klaudia, additional, Bader, Gary D, additional, Baier, Sylvie, additional, Blumenthal, David B, additional, Chen, Jing, additional, Elkjaer, Maria L, additional, Garcia-Hernandez, Carlos, additional, Helmy, Mohamed, additional, Hoffmann, Markus, additional, Jurisica, Igor, additional, Kotlyar, Max, additional, Lazareva, Olga, additional, Levi, Hagai, additional, List, Markus, additional, Lobentanzer, Sebastian, additional, Loscalzo, Joseph, additional, Malod-Dognin, Noel, additional, Manz, Quirin, additional, Matschinske, Julian, additional, Mee, Miles, additional, Oubounyt, Mhaned, additional, Pastrello, Chiara, additional, Pico, Alexander R, additional, Pillich, Rudolf T, additional, Poschenrieder, Julian M, additional, Pratt, Dexter, additional, Pržulj, Nataša, additional, Sadegh, Sepideh, additional, Saez-Rodriguez, Julio, additional, Sarkar, Suryadipto, additional, Shaked, Gideon, additional, Shamir, Ron, additional, Trummer, Nico, additional, Turhan, Ugur, additional, Wang, Rui-Sheng, additional, Zolotareva, Olga, additional, and Baumbach, Jan, additional

Published
2024
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43. A Map of Human Mitochondrial Protein Interactions Linked to Neurodegeneration Reveals New Mechanisms of Redox Homeostasis and NF-κB Signaling

Author

Malty, Ramy H, Aoki, Hiroyuki, Kumar, Ashwani, Phanse, Sadhna, Amin, Shahreen, Zhang, Qingzhou, Minic, Zoran, Goebels, Florian, Musso, Gabriel, Wu, Zhuoran, Abou-tok, Hosam, Meyer, Michael, Deineko, Viktor, Kassir, Sandy, Sidhu, Vishaldeep, Jessulat, Matthew, Scott, Nichollas E, Xiong, Xuejian, Vlasblom, James, Prasad, Bhanu, Foster, Leonard J, Alberio, Tiziana, Garavaglia, Barbara, Yu, Haiyuan, Bader, Gary D, Nakamura, Ken, Parkinson, John, and Babu, Mohan

Subjects
Biochemistry and Cell Biology, Biological Sciences, Neurodegenerative, Brain Disorders, Neurosciences, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Neurological, Animals, Autistic Disorder, Brain, HEK293 Cells, Humans, Mass Spectrometry, Mice, Mitochondria, Mitochondrial Proteins, NF-kappa B, Neurodegenerative Diseases, Neurons, Oxidation-Reduction, Protein Interaction Maps, Biochemistry and cell biology
Abstract

Mitochondrial protein (MP) dysfunction has been linked to neurodegenerative disorders (NDs); however, the discovery of the molecular mechanisms underlying NDs has been impeded by the limited characterization of interactions governing MP function. Here, using mass spectrometry (MS)-based analysis of 210 affinity-purified mitochondrial (mt) fractions isolated from 27 epitope-tagged human ND-linked MPs in HEK293 cells, we report a high-confidence MP network including 1,964 interactions among 772 proteins (>90% previously unreported). Nearly three-fourths of these interactions were confirmed in mouse brain and multiple human differentiated neuronal cell lines by primary antibody immunoprecipitation and MS, with many linked to NDs and autism. We show that the SOD1-PRDX5 interaction, critical for mt redox homeostasis, can be perturbed by amyotrophic lateral sclerosis-linked SOD1 allelic variants and establish a functional role for ND-linked factors coupled with IκBɛ in NF-κB activation. Our results identify mechanisms for ND-linked MPs and expand the human mt interaction landscape.

Published
2017

44. Intertumoral Heterogeneity within Medulloblastoma Subgroups

Author

Cavalli, Florence MG, Remke, Marc, Rampasek, Ladislav, Peacock, John, Shih, David JH, Luu, Betty, Garzia, Livia, Torchia, Jonathon, Nor, Carolina, Morrissy, A Sorana, Agnihotri, Sameer, Thompson, Yuan Yao, Kuzan-Fischer, Claudia M, Farooq, Hamza, Isaev, Keren, Daniels, Craig, Cho, Byung-Kyu, Kim, Seung-Ki, Wang, Kyu-Chang, Lee, Ji Yeoun, Grajkowska, Wieslawa A, Perek-Polnik, Marta, Vasiljevic, Alexandre, Faure-Conter, Cecile, Jouvet, Anne, Giannini, Caterina, Rao, Amulya A Nageswara, Li, Kay Ka Wai, Ng, Ho-Keung, Eberhart, Charles G, Pollack, Ian F, Hamilton, Ronald L, Gillespie, G Yancey, Olson, James M, Leary, Sarah, Weiss, William A, Lach, Boleslaw, Chambless, Lola B, Thompson, Reid C, Cooper, Michael K, Vibhakar, Rajeev, Hauser, Peter, van Veelen, Marie-Lise C, Kros, Johan M, French, Pim J, Ra, Young Shin, Kumabe, Toshihiro, López-Aguilar, Enrique, Zitterbart, Karel, Sterba, Jaroslav, Finocchiaro, Gaetano, Massimino, Maura, Van Meir, Erwin G, Osuka, Satoru, Shofuda, Tomoko, Klekner, Almos, Zollo, Massimo, Leonard, Jeffrey R, Rubin, Joshua B, Jabado, Nada, Albrecht, Steffen, Mora, Jaume, Van Meter, Timothy E, Jung, Shin, Moore, Andrew S, Hallahan, Andrew R, Chan, Jennifer A, Tirapelli, Daniela PC, Carlotti, Carlos G, Fouladi, Maryam, Pimentel, José, Faria, Claudia C, Saad, Ali G, Massimi, Luca, Liau, Linda M, Wheeler, Helen, Nakamura, Hideo, Elbabaa, Samer K, Perezpeña-Diazconti, Mario, de León, Fernando Chico Ponce, Robinson, Shenandoah, Zapotocky, Michal, Lassaletta, Alvaro, Huang, Annie, Hawkins, Cynthia E, Tabori, Uri, Bouffet, Eric, Bartels, Ute, Dirks, Peter B, Rutka, James T, Bader, Gary D, Reimand, Jüri, Goldenberg, Anna, Ramaswamy, Vijay, and Taylor, Michael D

Subjects
Genetics, Rare Diseases, Pediatric Cancer, Cancer, Brain Cancer, Pediatric Research Initiative, Pediatric, Human Genome, Brain Disorders, Cluster Analysis, Cohort Studies, DNA Copy Number Variations, DNA Methylation, Gene Expression Profiling, Genomics, Humans, Medulloblastoma, Precision Medicine, copy number, gene expression, integrative clustering, medulloblastoma, methylation, subgroups, Neurosciences, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

While molecular subgrouping has revolutionized medulloblastoma classification, the extent of heterogeneity within subgroups is unknown. Similarity network fusion (SNF) applied to genome-wide DNA methylation and gene expression data across 763 primary samples identifies very homogeneous clusters of patients, supporting the presence of medulloblastoma subtypes. After integration of somatic copy-number alterations, and clinical features specific to each cluster, we identify 12 different subtypes of medulloblastoma. Integrative analysis using SNF further delineates group 3 from group 4 medulloblastoma, which is not as readily apparent through analyses of individual data types. Two clear subtypes of infants with Sonic Hedgehog medulloblastoma with disparate outcomes and biology are identified. Medulloblastoma subtypes identified through integrative clustering have important implications for stratification of future clinical trials.

Published
2017

45. Drugst.One — a plug-and-play solution for online systems medicine and network-based drug repurposing

Author

Barcelona Supercomputing Center, Maier, Andreas, Hartung, Michael, Abovsky, Mark, Adamowicz, Klaudia, Bader, Gary D, Garcia Hernandez, Carlos, Malod Dognin, Noel, Przulj, Natasa, Barcelona Supercomputing Center, Maier, Andreas, Hartung, Michael, Abovsky, Mark, Adamowicz, Klaudia, Bader, Gary D, Garcia Hernandez, Carlos, Malod Dognin, Noel, and Przulj, Natasa

Abstract

In recent decades, the de v elopment of ne w drugs has become increasingly e xpensiv e and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools ha v e emerged to identify potential drug repurposing candidates. Ho w e v er, these tools often require complex installation and lack intuitive visual network mining capabilities. To tac kle these c hallenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing . W ith just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adapt abilit y, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://dr ugst.one , Dr ugst.One has significant potential for streamlining the drug disco v ery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research., REPO-TRIAL: this project has received funding from the European Union’s Horizon 2020 research and innovation programme [777111]; this publication reflects only the authors’ view and the European Commission is not responsible for any use that may be made of the information it contains; RePo4EU: this project is funded by the European Union [101057619]; views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible for them; Swiss State Secretariat for Education, Research and Innovation (SERI) [22.00115]; German Federal Ministry of Education and Research (BMBF) within the framework of ‘CLINSPECT-M/-2’ [F031L0214A, 161L0214A, 16LW0243K]; Technical University Munich – Institute for Advanced Study, funded by the German Excellence Initiative; Intramural Research Programs (IRPs) of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK); Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [422216132]; J.B. was partially funded by his VILLUM Young Investigator Grant [13154]; European Research Council (ERC) Consolidator Grant [770827]; Spanish State Research Agency AEI 10.13039/501100011033 [PID2019-105500GB-I00]; I.J. was supported in part by funding from Natural Sciences Research Council [NSERC #203475], Canada Foundation for Innovation [CFI #225404, #30865]; Ontario Research Fund [RDI #34876, RE010-020]; IBM and Ian Lawson van Toch Fund; S.L. has received funding from the European Union’s Horizon 2020 research and innovation programme [965193] for DECIDER. Funding for open access charge: Horizon Europe project Repo4EU., Peer Reviewed, Postprint (published version)

Published
2024

46. SIREN Cytoscape plugin: Interaction Type Discrimination in Gene Regulatory Networks

Author

Montojo, Jason, Khosravi, Pegah, Gazestani, Vahid H., and Bader, Gary D.

Subjects
Quantitative Biology - Molecular Networks
Abstract

Integrating expression data with gene interactions in a network is essential for understanding the functional organization of the cells. Consequently, knowledge of interaction types in biological networks is important for data interpretation. Signing of Regulatory Networks (SIREN) plugin for Cytoscape is an open-source Java tool for discrimination of interaction type (activatory or inhibitory) in gene regulatory networks. Utilizing an information theory based concept, SIREN seeks to identify the interaction type of pairs of genes by examining their corresponding gene expression profiles. We introduce SIREN, a fast and memory efficient tool with low computational complexity, that allows the user to easily consider it as a complementary approach for many network reconstruction methods. SIREN allows biologists to use independent expression data to predict interaction types for known gene regulatory networks where reconstruction methods do not provide any information about the nature of their interaction types. The SIREN Cytoscape plugin is implemented in Java and is freely available at http://baderlab.org/Software/SIRENplugin and via the Cytoscape app manager., Comment: 5 pages, 2 figures

Published
2015

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