bims-pideca Biomed News
on Class IA PI3K signalling in development and cancer
Issue of 2024–12–22
nineteen papers selected by
Ralitsa Radostinova Madsen, MRC-PPU
  1. Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome.
  2. Two Novel Red-FRET ERK Biosensors in the 670-720nm Range.
  3. A sensitive ERK fluorescent probe reveals the significance of minimal EGF-induced transcription.
  4. DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data.
  5. Systems Biology of the Cancer Cell.
  6. Precise kilobase-scale genomic insertions in mammalian cells using PASTE.
  7. CRISPR-StAR enables high-resolution genetic screening in complex in vivo models.
  8. Unveiling the power of high-dimensional cytometry data with cyCONDOR.
  9. Advancements in Global Phosphoproteomics Profiling: Overcoming Challenges in Sensitivity and Quantification.
  10. Pan-cancer analysis of biallelic inactivation in tumor suppressor genes identifies KEAP1 zygosity as a predictive biomarker in lung cancer.
  11. Casein kinase 1 controls components of a TORC2 signaling network in budding yeast.
  12. PIK3CA Mutations and Co-Mutations in Operated Non-Small Cell Lung Carcinoma.
  13. A consensus platform for antibody characterization.
  14. Spatial modeling algorithms for reactions and transport in biological cells.
  15. Hyperactivation of the PI3K pathway in inborn errors of immunity: current understanding and therapeutic perspectives.
  16. Methods for Visualizing and Quantifying Cholesterol Distribution in Mammalian Cells Using Filipin and D4 Probes.
  17. Origin of Chromosome 12 Trisomy Surge in Human Induced Pluripotent Stem Cells (iPSCs).
  18. Proximity adjusted centroid mapping for accurate detection of nuclei in dense 3D cell systems.
  19. DARPins as a novel tool to detect and degrade p73.
  1. Cell Syst. 2024 Dec 14. pii: S2405-4712(24)00366-1. [Epub ahead of print]
    Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome.
    Eli Metzner, Kaden M Southard, Thomas M Norman.
      Single-cell CRISPR screens link genetic perturbations to transcriptional states, but high-throughput methods connecting these induced changes to their regulatory foundations are limited. Here, we introduce Multiome Perturb-seq, extending single-cell CRISPR screens to simultaneously measure perturbation-induced changes in gene expression and chromatin accessibility. We apply Multiome Perturb-seq in a CRISPRi screen of 13 chromatin remodelers in human RPE-1 cells, achieving efficient assignment of sgRNA identities to single nuclei via an improved method for capturing barcode transcripts from nuclear RNA. We organize expression and accessibility measurements into coherent programs describing the integrated effects of perturbations on cell state, finding that ARID1A and SUZ12 knockdowns induce programs enriched for developmental features. Modeling of perturbation-induced heterogeneity connects accessibility changes to changes in gene expression, highlighting the value of multimodal profiling. Overall, our method provides a scalable and simply implemented system to dissect the regulatory logic underpinning cell state. A record of this paper's transparent peer review process is included in the supplemental information.
    Keywords:  CRISPRi; Perturb-seq; chromatin accessibility; functional genomics; gene regulatory networks; multiome; single-cell genomics
    DOI:  https://doi.org/10.1016/j.cels.2024.12.002
  2. bioRxiv. 2024 Dec 02. pii: 2024.11.30.626109. [Epub ahead of print]
    Two Novel Red-FRET ERK Biosensors in the 670-720nm Range.
    Nicholaus L DeCuzzi, Jason Y Hu, Florene Xu, Brayant Rodriguez, Michael Pargett, John G Albeck.
      Cell fate decisions are regulated by intricate signaling networks, with Extracellular signal-Regulated Kinase (ERK) being a central regulator. However, ERK is rarely the only signaling pathway involved, creating a need to study multiple signaling pathways simultaneously at the single-cell level. Many existing fluorescent biosensors for ERK and other pathways have significant spectral overlap, limiting their ability to be multiplexed. To address this limitation, we developed two novel red-FRET ERK biosensors, REKAR67 and REKAR76, which operate in the 670-720 nm range using miRFP670nano3 and miRFP720. REKAR67 and REKAR76 differ in fluorophore position, which impacts biosensor characteristics; REKAR67 displayed a higher dynamic range but greater signal variance than REKAR76. Mixed populations of REKAR67 or REKAR76 displayed similar Signal-to-Noise ratio (SNR), but in clonal cell populations, REKAR76 had a significantly higher SNR. Overall, our red-FRET ERK biosensors were highly consistent with existing ERK FRET biosensors and in reporting ERK activity and are spectrally compatible with CFP/YFP FRET and cpGFP -based biosensors. Both REKAR biosensors expand the available methods for measuring single-cell ERK activity.
    DOI:  https://doi.org/10.1101/2024.11.30.626109
  3. Cell Struct Funct. 2024 Dec 18.
    A sensitive ERK fluorescent probe reveals the significance of minimal EGF-induced transcription.
    Zhang Weisheng, Jun Nakayama, Yukino Inomata, Shigeki Higashiyama, Toru Hiratsuka.
      Extracellular signal-regulated kinase (ERK) regulates multiple cellular functions through distinct activation patterns. Genetically encoded fluorescent probes are instrumental in dissecting the ERK activity dynamics in living cells. Here we modified a previously reported Förster resonance energy transfer (FRET) probe for ERK, EKAREN5 by replacing its mTurquoise2 and YPet sequences with mTurquoise-GL and a synonymous codon variant of YPet, respectively. The modified biosensor, EKAREN5-gl showed an increased sensitivity to EGF-induced ERK activation responding to a very low dose (20 pg/ml) of EGF stimulation. We quantitatively characterized two FRET-based ERK probes, EKAREN5 and EKAREN5-gl, and a subcellular kinase translocation-based probe, ERK-KTR. We found the three biosensors differently respond to EGF stimulations with different intensity, duration, and latency. Furthermore, we investigated how the minimal EGF-induced ERK activation affects the downstream transcription in HeLa cells by comprehensive transcriptional analysis. We found the minimal ERK activation leads to a distinct transcriptional pattern from those induced by higher ERK activations. Our study highlights the significance of sensitive fluorescent probes to understand cellular signal dynamics and the role of minimal ERK activation in regulating transcription.Key words: fluorescent probe, ERK, FRET, KTR.
    Keywords:  ERK; FRET; KTR; fluorescent probe
    DOI:  https://doi.org/10.1247/csf.24070
  4. BMC Bioinformatics. 2024 Dec 18. 25(1): 383
    DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data.
    Christopher Patsalis, Gayatri Iyer, Marci Brandenburg, Alla Karnovsky, George Michailidis.
       BACKGROUND: Metabolomics is a high-throughput technology that measures small molecule metabolites in cells, tissues or biofluids. Analysis of metabolomics data is a multi-step process that involves data processing, quality control and normalization, followed by statistical and bioinformatics analysis. The latter step often involves pathway analysis to aid biological interpretation of the data. This approach is limited to endogenous metabolites that can be readily mapped to metabolic pathways. An alternative to pathway analysis that can be used for any classes of metabolites, including unknown compounds that are ubiquitous in untargeted metabolomics data, involves defining metabolite-metabolite interactions using experimental data. Our group has developed several network-based methods that use partial correlations of experimentally determined metabolite measurements. These were implemented in CorrelationCalculator and Filigree, two software tools for the analysis of metabolomics data we developed previously. The latter tool implements the Differential Network Enrichment Analysis (DNEA) algorithm. This analysis is useful for building differential networks from metabolomics data containing two experimental groups and identifying differentially enriched metabolic modules. While Filigree is a user-friendly tool, it has certain limitations when used for the analysis of large-scale metabolomics datasets.
    RESULTS: We developed the DNEA R package for the data-driven network analysis of metabolomics data. We present the DNEA workflow and functionality, algorithm enhancements implemented with respect to the package's predecessor, Filigree, and discuss best practices for analyses. We tested the performance of the DNEA R package and illustrated its features using publicly available metabolomics data from the environmental determinants of diabetes in the young. To our knowledge, this package is the only publicly available tool designed for the construction of biological networks and subsequent enrichment testing for datasets containing exogenous, secondary, and unknown compounds. This greatly expands the scope of traditional enrichment analysis tools that can be used to analyze a relatively small set of well-annotated metabolites.
    CONCLUSIONS: The DNEA R package is a more flexible and powerful implementation of our previously published software tool, Filigree. The modular structure of the package, along with the parallel processing framework built into the most computationally extensive steps of the algorithm, make it a powerful tool for the analysis of large and complex metabolomics datasets.
    Keywords:  Enrichment analysis; Metabolomics; Network analysis; Network visualization; Partial correlation; Pathway analysis
    DOI:  https://doi.org/10.1186/s12859-024-05994-1
  5. Annu Rev Biomed Eng. 2024 Dec 17.
    Systems Biology of the Cancer Cell.
    Kevin A Janes, Matthew J Lazzara.
      Questions in cancer have engaged systems biologists for decades. During that time, the quantity of molecular data has exploded, but the need for abstractions, formal models, and simplifying insights has remained the same. This review brings together classic breakthroughs and recent findings in the field of cancer systems biology, focusing on cancer-cell pathways for tumorigenesis and therapeutic response. Cancer cells mutate and transduce information from their environment to alter gene expression, metabolism, and phenotypic states. Understanding the molecular architectures that make each of these steps possible is a long-term goal of cancer systems biology pursued by iterating between quantitative models and experiments. We argue that such iteration is the best path to deploying targeted therapies intelligently so that each patient receives the maximum benefit for their cancer.
    DOI:  https://doi.org/10.1146/annurev-bioeng-103122-030552
  6. Nat Protoc. 2024 Dec 15.
    Precise kilobase-scale genomic insertions in mammalian cells using PASTE.
    Christopher W Fell, Cian Schmitt-Ulms, Dario V Tagliaferri, Jonathan S Gootenberg, Omar O Abudayyeh.
      Programmable gene integration technologies are an emerging modality with exciting applications in both basic research and therapeutic development. Programmable addition via site-specific targeting elements (PASTE) is a programmable gene integration approach for precise and efficient programmable integration of large DNA sequences into the genome. PASTE offers improved editing efficiency, purity and programmability compared with previous methods for long insertions into the mammalian genome. By combining the specificity and cargo size capabilities of site-specific integrases with the programmability of prime editing, PASTE can precisely insert cargoes of at least 36 kb with efficiencies of up to 60%. Here we outline best practices for design, execution and analysis of PASTE experiments, with protocols for integration of EGFP at the human NOLC1 and ACTB genomic loci and for readout by next generation sequencing and droplet digital PCR. We provide guidelines for designing and optimizing a custom PASTE experiment for integration of desired payloads at alternative genomic loci, as well as example applications for in-frame protein tagging and multiplexed insertions. To facilitate experimental setup, we include the necessary sequences and plasmids for the delivery of PASTE components to cells via plasmid transfection or in vitro transcribed RNA. Most experiments in this protocol can be performed in as little as 2 weeks, allowing for precise and versatile programmable gene insertion.
    DOI:  https://doi.org/10.1038/s41596-024-01090-z
  7. Nat Biotechnol. 2024 Dec 16.
    CRISPR-StAR enables high-resolution genetic screening in complex in vivo models.
    Esther C H Uijttewaal, Joonsun Lee, Annika Charlotte Sell, Naomi Botay, Gintautas Vainorius, Maria Novatchkova, Juliane Baar, Jiaye Yang, Tobias Potzler, Sophie van der Leij, Christopher Lowden, Julia Sinner, Anais Elewaut, Milanka Gavrilovic, Anna Obenauf, Daniel Schramek, Ulrich Elling.
      Pooled genetic screening with CRISPR-Cas9 has enabled genome-wide, high-resolution mapping of genes to phenotypes, but assessing the effect of a given genetic perturbation requires evaluation of each single guide RNA (sgRNA) in hundreds of cells to counter stochastic genetic drift and obtain robust results. However, resolution is limited in complex, heterogeneous models, such as organoids or tumors transplanted into mice, because achieving sufficient representation requires impractical scaling. This is due to bottleneck effects and biological heterogeneity of cell populations. Here we introduce CRISPR-StAR, a screening method that uses internal controls generated by activating sgRNAs in only half the progeny of each cell subsequent to re-expansion of the cell clone. Our method overcomes both intrinsic and extrinsic heterogeneity as well as genetic drift in bottlenecks by generating clonal, single-cell-derived intrinsic controls. We use CRISPR-StAR to identify in-vivo-specific genetic dependencies in a genome-wide screen in mouse melanoma. Benchmarking against conventional screening demonstrates the improved data quality provided by this technology.
    DOI:  https://doi.org/10.1038/s41587-024-02512-9
  8. Nat Commun. 2024 Dec 19. 15(1): 10702
    Unveiling the power of high-dimensional cytometry data with cyCONDOR.
    DELCODE Study Group
      High-dimensional cytometry (HDC) is a powerful technology for studying single-cell phenotypes in complex biological systems. Although technological developments and affordability have made HDC broadly available in recent years, technological advances were not coupled with an adequate development of analytical methods that can take full advantage of the complex data generated. While several analytical platforms and bioinformatics tools have become available for the analysis of HDC data, these are either web-hosted with limited scalability or designed for expert computational biologists, making their use unapproachable for wet lab scientists. Additionally, end-to-end HDC data analysis is further hampered due to missing unified analytical ecosystems, requiring researchers to navigate multiple platforms and software packages to complete the analysis. To bridge this data analysis gap in HDC we develop cyCONDOR, an easy-to-use computational framework covering not only all essential steps of cytometry data analysis but also including an array of downstream functions and tools to expand the biological interpretation of the data. The comprehensive suite of features of cyCONDOR, including guided pre-processing, clustering, dimensionality reduction, and machine learning algorithms, facilitates the seamless integration of cyCONDOR into clinically relevant settings, where scalability and disease classification are paramount for the widespread adoption of HDC in clinical practice. Additionally, the advanced analytical features of cyCONDOR, such as pseudotime analysis and batch integration, provide researchers with the tools to extract deeper insights from their data. We use cyCONDOR on a variety of data from different tissues and technologies demonstrating its versatility to assist the analysis of high-dimensional data from preprocessing to biological interpretation.
    DOI:  https://doi.org/10.1038/s41467-024-55179-w
  9. Proteomics. 2024 Dec 18. e202400087
    Advancements in Global Phosphoproteomics Profiling: Overcoming Challenges in Sensitivity and Quantification.
    Gul Muneer, Ciao-Syuan Chen, Yu-Ju Chen.
      Protein phosphorylation introduces post-genomic diversity to proteins, which plays a crucial role in various cellular activities. Elucidation of system-wide signaling cascades requires high-performance tools for precise identification and quantification of dynamics of site-specific phosphorylation events. Recent advances in phosphoproteomic technologies have enabled the comprehensive mapping of the dynamic phosphoproteomic landscape, which has opened new avenues for exploring cell type-specific functional networks underlying cellular functions and clinical phenotypes. Here, we provide an overview of the basics and challenges of phosphoproteomics, as well as the technological evolution and current state-of-the-art global and quantitative phosphoproteomics methodologies. With a specific focus on highly sensitive platforms, we summarize recent trends and innovations in miniaturized sample preparation strategies for micro-to-nanoscale and single-cell profiling, data-independent acquisition mass spectrometry (DIA-MS) for enhanced coverage, and quantitative phosphoproteomic pipelines for deep mapping of cell and disease biology. Each aspect of phosphoproteomic analysis presents unique challenges and opportunities for improvement and innovation. We specifically highlight evolving phosphoproteomic technologies that enable deep profiling from low-input samples. Finally, we discuss the persistent challenges in phosphoproteomic technologies, including the feasibility of nanoscale and single-cell phosphoproteomics, as well as future outlooks for biomedical applications.
    Keywords:  data‐independent acquisition; mass spectrometry; phosphoproteomics; protein phosphorylation
    DOI:  https://doi.org/10.1002/pmic.202400087
  10. Cell. 2024 Dec 12. pii: S0092-8674(24)01327-8. [Epub ahead of print]
    Pan-cancer analysis of biallelic inactivation in tumor suppressor genes identifies KEAP1 zygosity as a predictive biomarker in lung cancer.
    Mark Zucker, Maria A Perry, Samuel I Gould, Arielle Elkrief, Anton Safonov, Rohit Thummalapalli, Miika Mehine, Debyani Chakravarty, A Rose Brannon, Marc Ladanyi, Pedram Razavi, Mark T A Donoghue, Yonina R Murciano-Goroff, Kristiana Grigoriadis, Nicholas McGranahan, Mariam Jamal-Hanjani, Charles Swanton, Yuan Chen, Ronglai Shen, Sarat Chandarlapaty, David B Solit, Nikolaus Schultz, Michael F Berger, Jason Chang, Adam J Schoenfeld, Francisco J Sánchez-Rivera, Ed Reznik, Chaitanya Bandlamudi.
      The canonical model of tumor suppressor gene (TSG)-mediated oncogenesis posits that loss of both alleles is necessary for inactivation. Here, through allele-specific analysis of sequencing data from 48,179 cancer patients, we define the prevalence, selective pressure for, and functional consequences of biallelic inactivation across TSGs. TSGs largely assort into distinct classes associated with either pan-cancer (Class 1) or lineage-specific (Class 2) patterns of selection for biallelic loss, although some TSGs are predominantly monoallelically inactivated (Class 3/4). We demonstrate that selection for biallelic inactivation can be utilized to identify driver genes in non-canonical contexts, including among variants of unknown significance (VUSs) of several TSGs such as KEAP1. Genomic, functional, and clinical data collectively indicate that KEAP1 VUSs phenocopy established KEAP1 oncogenic alleles and that zygosity, rather than variant classification, is predictive of therapeutic response. TSG zygosity is therefore a fundamental determinant of disease etiology and therapeutic sensitivity.
    Keywords:  KEAP1; Knudson's two-hit; biallelic inactivation; biallelic loss; cancer genomics; clinical sequencing; lung cancer; pancancer; predictive biomarkers; tumor suppressor genes
    DOI:  https://doi.org/10.1016/j.cell.2024.11.010
  11. J Cell Sci. 2024 Dec 15. pii: jcs262036. [Epub ahead of print]137(24):
    Casein kinase 1 controls components of a TORC2 signaling network in budding yeast.
    Rafael Lucena, Akshi Jasani, Steph Anastasia, Douglas Kellogg, Maria Alcaide-Gavilan.
      Tor kinases play diverse and essential roles in control of nutrient signaling and cell growth. These kinases are assembled into two multiprotein complexes known as TORC1 and TORC2. In budding yeast, TORC2 relays nutrient-dependent signals that strongly influence growth rate and cell size. However, the mechanisms that control TORC2 signaling are poorly understood. Activation of TORC2 requires Mss4, a phosphatidylinositol 4-phosphate 5-kinase that recruits and activates downstream targets of TORC2. Localization of Mss4 to the plasma membrane is thought to be controlled by phosphorylation, and previous work has suggested that yeast homologs of casein kinase 1, Yck1 and Yck2 (referred to here collectively as Yck1/2), Control phosphorylation of Mss4. Here, we generated a new analog-sensitive allele of YCK2 and used it to test whether Yck1/2 influence localization of Mss4 or signaling in the TORC2 network. We found that Yck1/2 strongly influence Mss4 phosphorylation and localization, as well as influencing regulation of multiple components of the TORC2 network. However, inhibition of Yck1/2 causes mild effects on the best-characterized signaling axis in the TORC2 pathway, suggesting that Yck1/2 might play a larger role in influencing less well-understood aspects of TORC2 signaling.
    Keywords:  Casein kinase 1; Nutrients; PP2A; TORC2 signaling
    DOI:  https://doi.org/10.1242/jcs.262036
  12. J Clin Med. 2024 Dec 08. pii: 7472. [Epub ahead of print]13(23):
    PIK3CA Mutations and Co-Mutations in Operated Non-Small Cell Lung Carcinoma.
    Salih Cokpinar, Ibrahim Halil Erdogdu, Seda Orenay-Boyacioglu, Olcay Boyacioglu, Nesibe Kahraman-Cetin, Ibrahim Meteoglu.
      Background: Understanding PIK3CA mutations and co-mutations in non-small cell lung carcinoma (NSCLC) is critical to developing personalized treatment strategies. Therefore, this study aims to investigate PIK3CA mutations and the accompanying somatic variations in NSCLC. Methods: This retrospective study included 98 patients over 18 years of age who were diagnosed with NSCLC, operated on, and referred to the Molecular Pathology Laboratory between January 2019 and June 2024 for next-generation sequencing panel tests and ALK-ROS1 FISH analysis. Results: All patients were found to carry PIK3CA mutations. Among the 98 NSCLC patients analyzed, 16 (16.33%) were female and 82 (83.67%) were male. The average age of the patients was 64.53 ± 9.63 years, with an age range of 38-84 years, and the majority were 50 years or older. Of the cases, 51 presented the adenocarcinoma subtype, while the remaining 47 showed the squamous cell carcinoma subtype. A smoking history was present in 77 (78.57%) patients, while 21 (21.43%) had no smoking history. The most frequently detected pathogenic or likely pathogenic PIK3CA variations were c.1633G>A p.E545K (32.65%), c.1624G>A p.E542K (11.22%), c.3140A>G p.H1047R (11.22%), c.3140A>T p.H1047L (5.10%), c.1357G>C p.E453Q (4.08%), and c.3143A>G p.H1048R (2.04%). The top 10 mutations that most commonly accompanied PIK3CA variations were KRAS, NF1, TP53, EGFR, PTEN, BRAF, KIT, CDKN2A, SMARCA4, and ATM mutations, respectively. Conclusions:PIK3CA variations, along with other gene variations, may influence cancer progression and thus may play a crucial role in the determination of targeted treatment strategies.
    Keywords:  NSCLC; PIK3CA mutation; co-mutation; smoking history
    DOI:  https://doi.org/10.3390/jcm13237472
  13. Nat Protoc. 2024 Dec 17.
    A consensus platform for antibody characterization.
    Riham Ayoubi, Joel Ryan, Sara Gonzalez Bolivar, Charles Alende, Vera Ruiz Moleon, Maryam Fotouhi, Mona Alqazzaz, Kathleen Southern, Walaa Alshafie, Matt R Baker, Alexander R Ball, Danielle Callahan, Jeffery A Cooper, Katherine Crosby, Kevin J Harvey, Douglas W Houston, Ravindran Kumaran, Meghan Rego, Christine Schofield, Hai Wu, Michael S Biddle, Claire M Brown, Richard A Kahn, Anita Bandrowski, Harvinder S Virk, Aled M Edwards, Peter S McPherson, Carl Laflamme.
      Antibody-based research applications are critical for biological discovery. Yet there are no industry standards for comparing the performance of antibodies in various applications. We describe a knockout cell line-based antibody characterization platform, developed and approved jointly by industry and academic researchers, that enables the systematic comparison of antibody performance in western blot, immunoprecipitation and immunofluorescence. The scalable protocols, which require minimal technological resources, consist of (1) the identification of appropriate cell lines for antibody characterization studies, (2) development/contribution of isogenic knockout controls, and (3) a series of antibody characterization procedures focused on the most common applications of antibodies in research. We provide examples of expected outcomes to guide antibody users in evaluating antibody performance. Central to our approach is advocating for transparent and open data sharing, enabling a community effort to identify specific antibodies for all human proteins. Mid-level graduate students with training in biochemistry and prior experience in cell culture and microscopy can complete the protocols for a specific protein within 1 month while working part-time on this effort. Antibody characterization is needed to meet standards for resource validation and data reproducibility, which are increasingly required by journals and funding agencies.
    DOI:  https://doi.org/10.1038/s41596-024-01095-8
  14. Nat Comput Sci. 2024 Dec 19.
    Spatial modeling algorithms for reactions and transport in biological cells.
    Emmet A Francis, Justin G Laughlin, Jørgen S Dokken, Henrik N T Finsberg, Christopher T Lee, Marie E Rognes, Padmini Rangamani.
      Biological cells rely on precise spatiotemporal coordination of biochemical reactions to control their functions. Such cell signaling networks have been a common focus for mathematical models, but they remain challenging to simulate, particularly in realistic cell geometries. Here we present Spatial Modeling Algorithms for Reactions and Transport (SMART), a software package that takes in high-level user specifications about cell signaling networks and then assembles and solves the associated mathematical systems. SMART uses state-of-the-art finite element analysis, via the FEniCS Project software, to efficiently and accurately resolve cell signaling events over discretized cellular and subcellular geometries. We demonstrate its application to several different biological systems, including yes-associated protein (YAP)/PDZ-binding motif (TAZ) mechanotransduction, calcium signaling in neurons and cardiomyocytes, and ATP generation in mitochondria. Throughout, we utilize experimentally derived realistic cellular geometries represented by well-conditioned tetrahedral meshes. These scenarios demonstrate the applicability, flexibility, accuracy and efficiency of SMART across a range of temporal and spatial scales.
    DOI:  https://doi.org/10.1038/s43588-024-00745-x
  15. Immunother Adv. 2024 ;4(1): ltae009
    Hyperactivation of the PI3K pathway in inborn errors of immunity: current understanding and therapeutic perspectives.
    Hanna IJspeert, Virgil A S H Dalm, Menno C van Zelm, Emily S J Edwards.
      The phosphoinositide-3-kinase (PI3K) pathway function is crucial to the normal development, differentiation, and function of immune cells including B, T, and NK cells. Following the description of two cohorts of patients with an inboirn error of immunity (also known as primary immunodeficiency) with gain-of-function variants in the PIK3CD gene a decade ago, the disease entity activated PI3K delta syndrome (APDS) was named. Since then, many more patients with PIK3CD variants have been described, and loss-of-function variants in PIK3R1 and PTEN have also been linked to APDS. Importantly, the availability of small molecules that inhibit the PI3K pathway has enabled targeted treatment of APDS patients. In this review, we define (i) the PI3K pathway and its role in inborn errors of immunity; (ii) the clinical and immunological presentation of APDS1 (PIK3CD GOF), APDS2 (PIK3R1 LOF), and related disorders; (iii) Diagnostic approaches to identify and functionally validate the genetic causes of disease; (iv) therapeutic interventions to target PI3K hyperactivation; and finally (v) current challenges and future perspectives that require attention for the optimal treatment of patients with APDS and APDS-L diseases.
    Keywords:  APDS-like; PI3K pathway; activated PI3K delta syndrome; immunodeficiency; immunotherapy
    DOI:  https://doi.org/10.1093/immadv/ltae009
  16. Methods Mol Biol. 2025 ;2888 101-118
    Methods for Visualizing and Quantifying Cholesterol Distribution in Mammalian Cells Using Filipin and D4 Probes.
    Julie Eichler, Sophie Huver, Céline J Knorr, Corinne Wendling, Toshihide Kobayashi, Catherine Tomasetto, Fabien Alpy.
      Cholesterol is a key component of biological membranes and, like many cellular lipids, is unevenly distributed among organelles. Disruptions in cholesterol trafficking are associated with various pathologies, including lysosomal lipid storage disorders, often characterized by intracellular cholesterol accumulation. A significant challenge in studying cholesterol trafficking is the lack of easy methods to trace this molecule in situ. Fluorescent probes that specifically bind cholesterol have enabled the visualization and imaging of cholesterol distribution within cells. This chapter details optimized methods for visualizing and quantifying free cholesterol at the plasma membrane and intracellulaly, both in individual cells and in large cell populations. These methods use two fluorescent probes: the D4 fragment of perfringolysin O fused to monomeric EGFP (mEGFP-D4 and the more sensitive mutant mEGFP-D4H) and the polyene macrolide filipin. We describe robust methods for quantifying plasma membrane cholesterol by flow cytometry and to visualize intracellular cholesterol pools by light microscopy. Furthermore, we introduce a refined filipin staining protocol that enhances intracellular cholesterol detection. For precise quantification, we developed an automated image analysis pipeline. This chapter provides a comprehensive guide for staining and quantifying cellular cholesterol, offering valuable tools for studying cholesterol dynamics in mammalian cells.
    Keywords:  Cholesterol; D4 probe; Filipin; Late endosome; Lysosome; Perfringolysin O; Plasma membrane
    DOI:  https://doi.org/10.1007/978-1-0716-4318-1_8
  17. bioRxiv. 2024 Dec 11. pii: 2024.12.02.626470. [Epub ahead of print]
    Origin of Chromosome 12 Trisomy Surge in Human Induced Pluripotent Stem Cells (iPSCs).
    Maria Narozna, Megan C Latham, Gary J Gorbsky.
      Cultured pluripotent stem cells are unique in being the only fully diploid immortal human cell lines. However, during continued culture they can acquire significant chromosome abnormalities. Chromosome 12 trisomy is the most common whole-chromosome abnormality found during culture of human induced pluripotent stem cells (iPSCs). The conventional paradigm is that trisomy 12 occurs very rarely but provides a proliferative advantage, enabling these cells to outcompete the diploid. Here, we challenge this prevailing model by demonstrating that trisomy 12 can arise simultaneously during mitosis in a high percentage (~2%) of diploid iPSCs. Using a single cell line that reproducibly undergoes transition from diploid to trisomy 12, we found that proliferation differences alone could not account for the rapid dominance of trisomic cells. Through careful mapping by fluorescent in-situ hybridization (FISH), we identified critical transition passages where trisomic cells first appeared and swiftly gained dominance. Remarkably, single trisomic cells repeatedly emerged de novo from diploid parents. Delving deeper, we discovered an extremely high incidence of chromosome 12 anaphase bridging exclusively during transition passages, along with overrepresentation of chromosome 12 chromatids in micronuclei. These micronuclei failed to replicate during S phase, leading to M phase cells containing two normal replicated copies of chromosome 12 and one unreplicated single chromatid. Consequently, we observed 1.5% of metaphase cells with an unpaired chromosome 12 chromatid positioned outside the metaphase plate, and 2% anaphase cells showing three chromosome 12 signals oriented to one pole and two to the other. Further analysis revealed that nearly 20% of subtelomeric repeats were eroded in the p arms but not q arms of chromosome 12 during transition passages. We found that p arm fusions were exclusively responsible for the chromosome 12 bridging observed in anaphase cells of transition passages. Our findings unveil a novel mechanism of whole-chromosome instability in iPSCs, where chromosome 12p arm-specific segregation errors occur simultaneously in a high percentage of cells rather than originating from single, rare events. The slight yet significant growth advantage of trisomy 12 cells allows them to persist and eventually dominate the population. This discovery has profound implications for pinpointing origins of chromosome instability during culture of iPSCs and helping to promote their effective use in research and regenerative medicine.
    DOI:  https://doi.org/10.1101/2024.12.02.626470
  18. Comput Biol Med. 2024 Dec 17. pii: S0010-4825(24)01646-9. [Epub ahead of print]185 109561
    Proximity adjusted centroid mapping for accurate detection of nuclei in dense 3D cell systems.
    Tim Van De Looverbosch, Sarah De Beuckeleer, Frederik De Smet, Jan Sijbers, Winnok H De Vos.
      In the past decade, deep learning algorithms have surpassed the performance of many conventional image segmentation pipelines. Powerful models are now available for segmenting cells and nuclei in diverse 2D image types, but segmentation in 3D cell systems remains challenging due to the high cell density, the heterogenous resolution and contrast across the image volume, and the difficulty in generating reliable and sufficient ground truth data for model training. Reasoning that most image processing applications rely on nuclear segmentation but do not necessarily require an accurate delineation of their shapes, we implemented Proximity Adjusted Centroid MAPping (PAC-MAP), a 3D U-net based method that predicts the position of nuclear centroids and their proximity to other nuclei. We show that our model outperforms existing methods, predominantly by boosting recall, especially in conditions of high cell density. When trained from scratch with limited expert annotations (30 images), PAC-MAP attained an average F1 score of 0.793 for nuclei centroid prediction in dense spheroids. When pretraining using weakly supervised bulk data (>2300 images) followed by finetuning with the available expert annotations, the average F1 score could be significantly improved to 0.816. We demonstrate the utility of our method for quantifying the absolute cell content of spheroids and comprehensively mapping the infiltration pattern of patient-derived glioblastoma cells in cerebral organoids.
    DOI:  https://doi.org/10.1016/j.compbiomed.2024.109561
  19. Cell Death Dis. 2024 Dec 18. 15(12): 909
    DARPins as a novel tool to detect and degrade p73.
    Philipp Münick, Jasmin Zielinski, Alexander Strubel, Niklas Gutfreund, Birgit Dreier, Jonas V Schaefer, Birgit Schäfer, Jakob Gebel, Christian Osterburg, Apirat Chaikuad, Stefan Knapp, Andreas Plückthun, Volker Dötsch.
      The concept of Targeted Protein Degradation (TPD) has been introduced as an attractive alternative to the development of classical inhibitors. TPD can extend the range of proteins that can be pharmacologically targeted beyond the classical targets for small molecule inhibitors, as a binding pocket is required but its occupancy does not need to lead to inhibition. The method is based on either small molecules that simultaneously bind to a protein of interest and to a cellular E3 ligase and bring them in close proximity (molecular glue) or a bi-functional molecule synthesized from the chemical linkage of a target protein-specific small molecule and one that binds to an E3 ligase (Proteolysis Targeting Chimeras (PROTAC)). The further extension of this approach to bioPROTACs, in which a small protein-based binding module is fused directly to an E3 ligase or an E3 ligase adaptor protein, makes virtually all proteins amenable to targeted degradation, as this method eliminates the requirement for binding pockets for small molecules. Designed Ankyrin Repeat Proteins (DARPins) represent a very attractive class of small protein-based binding modules that can be used for the development of bioPTOTACS. Here we describe the characterization of two DARPins generated against the oligomerization domain and the SAM domain of the transcription factor p73, a member of the p53 protein family. The DARPins can be used for (isoform-)selective pulldown experiments both in cell culture as well as primary tissue lysates. We also demonstrate that they can be used for staining in cell culture experiments. Fusing them to the speckle type POZ protein (SPOP), an adaptor protein for cullin-3 E3 ligase complexes, yields highly selective and effective degraders. We demonstrate that selective degradation of the ΔNp73α isoform reactivates p53.
    DOI:  https://doi.org/10.1038/s41419-024-07304-2
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