bims-gerecp Biomed News
on Gene regulatory networks of epithelial cell plasticity
Issue of 2024–12–15
eightteen papers selected by
Xiao Qin, University of Oxford
  1. Considerations for building and using integrated single-cell atlases.
  2. Optimized reporters for multiplexed detection of transcription factor activity.
  3. Chromatin topology dynamics in gene activation.
  4. How to build the virtual cell with artificial intelligence: Priorities and opportunities.
  5. Heterogeneity-driven phenotypic plasticity and treatment response in branched-organoid models of pancreatic ductal adenocarcinoma.
  6. Adaptable CRISPR screening.
  7. Stem cells.
  8. Organoids get an upgrade.
  9. How Good Is Good Enough? What Should Be the Target for CRC Screening?
  10. Mapping cell history and fates.
  11. Paired CRISPR screens to map gene regulation in cis and trans.
  12. A framework for neural organoids, assembloids and transplantation studies.
  13. Key Regulatory Elements of the TGFβ-LRRC15 Axis Predict Disease Progression and Immunotherapy Resistance Across Cancer Types.
  14. A statistical approach for systematic identification of transition cells from scRNA-seq data.
  15. Modelling human brain development and disease with organoids.
  16. Method of the Year 2024: spatial proteomics.
  17. Breaking the mold: 3D cell cultures reshaping the future of cancer research.
  18. Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics.
  1. Nat Methods. 2024 Dec 13.
    Considerations for building and using integrated single-cell atlases.
    Karin Hrovatin, Lisa Sikkema, Vladimir A Shitov, Graham Heimberg, Maiia Shulman, Amanda J Oliver, Michaela F Mueller, Ignacio L Ibarra, Hanchen Wang, Ciro Ramírez-Suástegui, Peng He, Anna C Schaar, Sarah A Teichmann, Fabian J Theis, Malte D Luecken.
      The rapid adoption of single-cell technologies has created an opportunity to build single-cell 'atlases' integrating diverse datasets across many laboratories. Such atlases can serve as a reference for analyzing and interpreting current and future data. However, it has become apparent that atlasing approaches differ, and the impact of these differences are often unclear. Here we review the current atlasing literature and present considerations for building and using atlases. Importantly, we find that no one-size-fits-all protocol for atlas building exists, but rather we discuss context-specific considerations and workflows, including atlas conceptualization, data collection, curation and integration, atlas evaluation and atlas sharing. We further highlight the benefits of integrated atlases for analyses of new datasets and deriving biological insights beyond what is possible from individual datasets. Our overview of current practices and associated recommendations will improve the quality of atlases to come, facilitating the shift to a unified, reference-based understanding of single-cell biology.
    DOI:  https://doi.org/10.1038/s41592-024-02532-y
  2. Cell Syst. 2024 Dec 03. pii: S2405-4712(24)00312-0. [Epub ahead of print]
    Optimized reporters for multiplexed detection of transcription factor activity.
    Max Trauernicht, Teodora Filipovska, Chaitanya Rastogi, Bas van Steensel.
      In any given cell type, dozens of transcription factors (TFs) act in concert to control the activity of the genome by binding to specific DNA sequences in regulatory elements. Despite their considerable importance, we currently lack simple tools to directly measure the activity of many TFs in parallel. Massively parallel reporter assays (MPRAs) allow the detection of TF activities in a multiplexed fashion; however, we lack basic understanding to rationally design sensitive reporters for many TFs. Here, we use an MPRA to systematically optimize transcriptional reporters for 86 TFs and evaluate the specificity of all reporters across a wide array of TF perturbation conditions. We thus identified critical TF reporter design features and obtained highly sensitive and specific reporters for 62 TFs, many of which outperform available reporters. The resulting collection of "prime" TF reporters can be used to uncover TF regulatory networks and to illuminate signaling pathways. A record of this paper's transparent peer review process is included in the supplemental information.
    Keywords:  MPRA; TF; TF reporter assay; TF reporter design; massively parallel reporter assay; multiplexed TF reporter assay; reporter; signaling pathways; specificity; transcription factor
    DOI:  https://doi.org/10.1016/j.cels.2024.11.003
  3. Nat Genet. 2024 Dec;56(12): 2589
    Chromatin topology dynamics in gene activation.
    Petra Gross.
      
    DOI:  https://doi.org/10.1038/s41588-024-02041-w
  4. Cell. 2024 Dec 12. pii: S0092-8674(24)01332-1. [Epub ahead of print]187(25): 7045-7063
    How to build the virtual cell with artificial intelligence: Priorities and opportunities.
    Charlotte Bunne, Yusuf Roohani, Yanay Rosen, Ankit Gupta, Xikun Zhang, Marcel Roed, Theo Alexandrov, Mohammed AlQuraishi, Patricia Brennan, Daniel B Burkhardt, Andrea Califano, Jonah Cool, Abby F Dernburg, Kirsty Ewing, Emily B Fox, Matthias Haury, Amy E Herr, Eric Horvitz, Patrick D Hsu, Viren Jain, Gregory R Johnson, Thomas Kalil, David R Kelley, Shana O Kelley, Anna Kreshuk, Tim Mitchison, Stephani Otte, Jay Shendure, Nicholas J Sofroniew, Fabian Theis, Christina V Theodoris, Srigokul Upadhyayula, Marc Valer, Bo Wang, Eric Xing, Serena Yeung-Levy, Marinka Zitnik, Theofanis Karaletsos, Aviv Regev, Emma Lundberg, Jure Leskovec, Stephen R Quake.
      Cells are essential to understanding health and disease, yet traditional models fall short of modeling and simulating their function and behavior. Advances in AI and omics offer groundbreaking opportunities to create an AI virtual cell (AIVC), a multi-scale, multi-modal large-neural-network-based model that can represent and simulate the behavior of molecules, cells, and tissues across diverse states. This Perspective provides a vision on their design and how collaborative efforts to build AIVCs will transform biological research by allowing high-fidelity simulations, accelerating discoveries, and guiding experimental studies, offering new opportunities for understanding cellular functions and fostering interdisciplinary collaborations in open science.
    Keywords:  AI; ML; cell biology; virtual cell
    DOI:  https://doi.org/10.1016/j.cell.2024.11.015
  5. Nat Biomed Eng. 2024 Dec 10.
    Heterogeneity-driven phenotypic plasticity and treatment response in branched-organoid models of pancreatic ductal adenocarcinoma.
    Aristeidis Papargyriou, Mulham Najajreh, David P Cook, Carlo H Maurer, Stefanie Bärthel, Hendrik A Messal, Sakthi K Ravichandran, Till Richter, Moritz Knolle, Thomas Metzler, Akul R Shastri, Rupert Öllinger, Jacob Jasper, Laura Schmidleitner, Surui Wang, Christian Schneeweis, Hellen Ishikawa-Ankerhold, Thomas Engleitner, Laura Mataite, Mariia Semina, Hussein Trabulssi, Sebastian Lange, Aashreya Ravichandra, Maximilian Schuster, Sebastian Mueller, Katja Peschke, Arlett Schäfer, Sophie Dobiasch, Stephanie E Combs, Roland M Schmid, Andreas R Bausch, Rickmer Braren, Irina Heid, Christina H Scheel, Günter Schneider, Anja Zeigerer, Malte D Luecken, Katja Steiger, Georgios Kaissis, Jacco van Rheenen, Fabian J Theis, Dieter Saur, Roland Rad, Maximilian Reichert.
      In patients with pancreatic ductal adenocarcinoma (PDAC), intratumoural and intertumoural heterogeneity increases chemoresistance and mortality rates. However, such morphological and phenotypic diversities are not typically captured by organoid models of PDAC. Here we show that branched organoids embedded in collagen gels can recapitulate the phenotypic landscape seen in murine and human PDAC, that the pronounced molecular and morphological intratumoural and intertumoural heterogeneity of organoids is governed by defined transcriptional programmes (notably, epithelial-to-mesenchymal plasticity), and that different organoid phenotypes represent distinct tumour-cell states with unique biological features in vivo. We also show that phenotype-specific therapeutic vulnerabilities and modes of treatment-induced phenotype reprogramming can be captured in phenotypic heterogeneity maps. Our methodology and analyses of tumour-cell heterogeneity in PDAC may guide the development of phenotype-targeted treatment strategies.
    DOI:  https://doi.org/10.1038/s41551-024-01273-9
  6. Nat Methods. 2024 Dec;21(12): 2226
    Adaptable CRISPR screening.
    Lei Tang.
      
    DOI:  https://doi.org/10.1038/s41592-024-02544-8
  7. Nat Biotechnol. 2024 Dec;42(12): 1792
    Stem cells.
      
    DOI:  https://doi.org/10.1038/s41587-024-02496-6
  8. Nat Methods. 2024 Dec;21(12): 2228-2229
    Organoids get an upgrade.
    Madhura Mukhopadhyay.
      
    DOI:  https://doi.org/10.1038/s41592-024-02549-3
  9. Dig Dis Sci. 2024 Dec 13.
    How Good Is Good Enough? What Should Be the Target for CRC Screening?
    David Lieberman, Evelien Dekker.
      Colorectal Cancer (CRC) screening of average-risk individuals has been shown to reduce CRC mortality and incidence. Incidence is reduced by detection and removal of cancer precursor lesions (CPLs), resulting in cancer prevention. Mortality reduction is achieved with detection of curable CRC, as well as prevention by removing CPLs before cancer develops. Targets of screening include both curable CRC and CPLs. Non-invasive stool tests are a multi-step screening program which can detect curable cancers and less likely to detect CPLs than invasive screening. The non-invasive programs depend on completion of colonoscopy if the test is positive. Invasive screening with colonoscopy is a one-step test program, with excellent detection of both curable CRCs and CPLs, if performed with high-quality. Current evidence suggests that either program could be effective, despite different targets. Patient adherence and program quality are perhaps the important determinants of program effectiveness.
    Keywords:  Cancer precursor lesions (adenomas and sessile serrated lesions); Colonoscopy; Colorectal cancer; Colorectal cancer prevention; Colorectal cancer screening
    DOI:  https://doi.org/10.1007/s10620-024-08698-x
  10. Nat Methods. 2024 Dec;21(12): 2227-2228
    Mapping cell history and fates.
    Lin Tang.
      
    DOI:  https://doi.org/10.1038/s41592-024-02547-5
  11. bioRxiv. 2024 Nov 27. pii: 2024.11.27.625752. [Epub ahead of print]
    Paired CRISPR screens to map gene regulation in cis and trans.
    Xinhe Xue, Zoran Z Gajic, Christina M Caragine, Mateusz Legut, Conor Walker, James Y S Kim, Xiao Wang, Rachel E Yan, Hans-Hermann Wessels, Congyi Lu, Neil Bapodra, Gamze Gürsoy, Neville E Sanjana.
      Recent massively-parallel approaches to decipher gene regulatory circuits have focused on the discovery of either cis -regulatory elements (CREs) or trans -acting factors. Here, we develop a scalable approach that pairs cis - and trans -regulatory CRISPR screens to systematically dissect how the key immune checkpoint PD-L1 is regulated. In human pancreatic ductal adenocarcinoma (PDAC) cells, we tile the PD-L1 locus using ∼25,000 CRISPR perturbations in constitutive and IFNγ-stimulated conditions. We discover 67 enhancer- or repressor-like CREs and show that distal CREs tend to contact the promoter of PD-L1 and related genes. Next, we measure how loss of all ∼2,000 transcription factors (TFs) in the human genome impacts PD-L1 expression and, using this, we link specific TFs to individual CREs and reveal novel PD-L1 regulatory circuits. For one of these regulatory circuits, we confirm the binding of predicted trans -factors (SRF and BPTF) using CUT&RUN and show that loss of either the CRE or TFs potentiates the anti-cancer activity of primary T cells engineered with a chimeric antigen receptor. Finally, we show that expression of these TFs correlates with PD-L1 expression in vivo in primary PDAC tumors and that somatic mutations in TFs can alter response and overall survival in immune checkpoint blockade-treated patients. Taken together, our approach establishes a generalizable toolkit for decoding the regulatory landscape of any gene or locus in the human genome, yielding insights into gene regulation and clinical impact.
    DOI:  https://doi.org/10.1101/2024.11.27.625752
  12. Nature. 2024 Dec 09.
    A framework for neural organoids, assembloids and transplantation studies.
    Sergiu P Pașca, Paola Arlotta, Helen S Bateup, J Gray Camp, Silvia Cappello, Fred H Gage, Jürgen A Knoblich, Arnold R Kriegstein, Madeline A Lancaster, Guo-Li Ming, Gaia Novarino, Hideyuki Okano, Malin Parmar, In-Hyun Park, Orly Reiner, Hongjun Song, Lorenz Studer, Jun Takahashi, Sally Temple, Giuseppe Testa, Barbara Treutlein, Flora M Vaccarino, Pierre Vanderhaeghen, Tracy Young-Pearse.
      As the field of neural organoids and assembloids rapidly expands, there is an emergent need for guidance and advice on designing, conducting and reporting experiments to increase the reproducibility and utility of these models. Here, our consortium- representing specialized laboratories from around the world- presents a framework for the experimental process that ranges from ensuring the quality and integrity of human pluripotent stem cells to characterizing and manipulating neural cells in vitro, and from transplantation techniques to considerations for modeling human development, evolution, and disease. As with all scientific endeavors, we advocate for rigorous experimental designs tailored to explicit scientific questions, and transparent methodologies and data sharing, to provide useful knowledge for both current research practices and for developing regulatory standards.
    DOI:  https://doi.org/10.1038/s41586-024-08487-6
  13. bioRxiv. 2024 Nov 25. pii: 2024.11.22.624939. [Epub ahead of print]
    Key Regulatory Elements of the TGFβ-LRRC15 Axis Predict Disease Progression and Immunotherapy Resistance Across Cancer Types.
    Claire M Storey, Michael Cheng, Mohamed Altai, Julie E Park, Julie Tran, Smiths S Lueong, Daniel Thorek, Liqun Mao, Wahed Zedan, Constance Yuen, Alexander Ridley, Marija Trajovic-Arsic, Ken Herrmann, Sumit K Subuhdi, Bilal A Siddiqui, Katharina Lückerath, Jens Siveke, Robert Damoiseaux, Xia Yang, David Ulmert.
      Transforming growth factor-beta (TGFβ) has dual roles in cancer, initially suppressing tumors but later promoting metastasis and immune evasion. Efforts to inhibit TGFβ have been largely unsuccessful due to significant toxicity and indiscriminate immunosuppression. Leucine-rich repeat-containing protein 15 (LRRC15) is a TGFβ-regulated antigen expressed by mesenchymal-derived cancer cells and cancer-associated fibroblasts (CAFs). In preclinical studies, ablation of TGFβ-driven LRRC15+ CAFs increased tumor infiltration of CD8+ T cells. However, the underlying pathobiological mechanisms prompting TGFβ's upregulation of LRRC15 expression are unclear. Using an integrated approach combining functional compound screening with single-cell RNA sequencing, we reveal key genomic features regulating TGFβ's ability to increase LRRC15 expression on cancer cells. Construction of gene regulatory networks converged our analyses on four key genes- MMP2, SPARC, TGF β R2, and WNT5B -central to TGFβ-induced LRRC15 pathobiology. Validation of these genes in cell models and their use in predicting immunotherapy responses highlight their potential in refining immunotherapy strategies and personalizing co-treatment options.
    DOI:  https://doi.org/10.1101/2024.11.22.624939
  14. Cell Rep Methods. 2024 Nov 28. pii: S2667-2375(24)00303-5. [Epub ahead of print] 100913
    A statistical approach for systematic identification of transition cells from scRNA-seq data.
    Yuanxin Wang, Merve Dede, Vakul Mohanty, Jinzhuang Dou, Ziyi Li, Ken Chen.
      Decoding cellular state transitions is crucial for understanding complex biological processes in development and disease. While recent advancements in single-cell RNA sequencing (scRNA-seq) offer insights into cellular trajectories, existing tools primarily study expressional rather than regulatory state shifts. We present CellTran, a statistical approach utilizing paired-gene expression correlations to detect transition cells from scRNA-seq data without explicitly resolving gene regulatory networks. Applying our approach to various contexts, including tissue regeneration, embryonic development, preinvasive lesions, and humoral responses post-vaccination, reveals transition cells and their distinct gene expression profiles. Our study sheds light on the underlying molecular mechanisms driving cellular state transitions, enhancing our ability to identify therapeutic targets for disease interventions.
    Keywords:  CP: developmental biology; CP: systems biology; carcinogenesis; cell development; cell differentiation; cell transitions; differential equations; dynamic systems; gene expression correlation; gene regulatory network; single-cell RNA sequencing; statistical analysis
    DOI:  https://doi.org/10.1016/j.crmeth.2024.100913
  15. Nat Rev Mol Cell Biol. 2024 Dec 12.
    Modelling human brain development and disease with organoids.
    Marcella Birtele, Madeline Lancaster, Giorgia Quadrato.
      Organoids are systems derived from pluripotent stem cells at the interface between traditional monolayer cultures and in vivo animal models. The structural and functional characteristics of organoids enable the modelling of early stages of brain development in a physiologically relevant 3D environment. Moreover, organoids constitute a tool with which to analyse how individual genetic variation contributes to the susceptibility and progression of neurodevelopmental disorders. This Roadmap article describes the features of brain organoids, focusing on the neocortex, and their advantages and limitations - in comparison with other model systems - for the study of brain development, evolution and disease. We highlight avenues for enhancing the physiological relevance of brain organoids by integrating bioengineering techniques and unbiased high-throughput analyses, and discuss future applications. As organoids advance in mimicking human brain functions, we address the ethical and societal implications of this technology.
    DOI:  https://doi.org/10.1038/s41580-024-00804-1
  16. Nat Methods. 2024 Dec;21(12): 2195-2196
    Method of the Year 2024: spatial proteomics.
      
    DOI:  https://doi.org/10.1038/s41592-024-02565-3
  17. Front Cell Dev Biol. 2024 ;12 1507388
    Breaking the mold: 3D cell cultures reshaping the future of cancer research.
    Sandra Cordeiro, Beatriz B Oliveira, Ruben Valente, Daniela Ferreira, André Luz, Pedro V Baptista, Alexandra R Fernandes.
      Despite extensive efforts to unravel tumor behavior and develop anticancer therapies, most treatments fail when advanced to clinical trials. The main challenge in cancer research has been the absence of predictive cancer models, accurately mimicking the tumoral processes and response to treatments. The tumor microenvironment (TME) shows several human-specific physical and chemical properties, which cannot be fully recapitulated by the conventional 2D cell cultures or the in vivo animal models. These limitations have driven the development of novel in vitro cancer models, that get one step closer to the typical features of in vivo systems while showing better species relevance. This review introduces the main considerations required for developing and exploiting tumor spheroids and organoids as cancer models. We also detailed their applications in drug screening and personalized medicine. Further, we show the transition of these models into novel microfluidic platforms, for improved control over physiological parameters and high-throughput screening. 3D culture models have provided key insights into tumor biology, more closely resembling the in vivo TME and tumor characteristics, while enabling the development of more reliable and precise anticancer therapies.
    Keywords:  3D models; chips; drug screening; patient-derived organoids (PDOs); personalized medicine; spheroids
    DOI:  https://doi.org/10.3389/fcell.2024.1507388
  18. Nat Cancer. 2024 Dec 11.
    Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics.
    Zhiao Shi, Jonathan T Lei, John M Elizarraras, Bing Zhang.
      Large-scale omics profiling has uncovered a vast array of somatic mutations and cancer-associated proteins, posing substantial challenges for their functional interpretation. Here we present a network-based approach centered on FunMap, a pan-cancer functional network constructed using supervised machine learning on extensive proteomics and RNA sequencing data from 1,194 individuals spanning 11 cancer types. Comprising 10,525 protein-coding genes, FunMap connects functionally associated genes with unprecedented precision, surpassing traditional protein-protein interaction maps. Network analysis identifies functional protein modules, reveals a hierarchical structure linked to cancer hallmarks and clinical phenotypes, provides deeper insights into established cancer drivers and predicts functions for understudied cancer-associated proteins. Additionally, applying graph-neural-network-based deep learning to FunMap uncovers drivers with low mutation frequency. This study establishes FunMap as a powerful and unbiased tool for interpreting somatic mutations and understudied proteins, with broad implications for advancing cancer biology and informing therapeutic strategies.
    DOI:  https://doi.org/10.1038/s43018-024-00869-z
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