Quantitative imaging of loop extruders rebuilding interphase genome architecture after mitosis.

How cells establish the interphase genome organization after mitosis is incompletely understood. Using quantitative and super-resolution microscopy, we show that the transition from a Condensin to a Cohesin-based genome organization occurs dynamically over two hours. While a significant fraction of Condensins remains chromatin-bound until early G1, Cohesin-STAG1 and its boundary factor CTCF are rapidly imported into daughter nuclei in telophase, immediately bind chromosomes as individual complexes and are sufficient to build the first interphase TAD structures. By contrast, the more abundant Cohesin-STAG2 accumulates on chromosomes only gradually later in G1, is responsible for compaction inside TAD structures and forms paired complexes upon completed nuclear import. Our quantitative time-resolved mapping of mitotic and interphase loop extruders in single cells reveals that the nested loop architecture formed by sequential action of two Condensins in mitosis is seamlessly replaced by a less compact, but conceptually similar hierarchically nested loop architecture driven by sequential action of two Cohesins.

mARC1 in MASLD: Modulation of lipid accumulation in human hepatocytes and adipocytes.

BACKGROUND: Mutations in the gene MTARC1 (mitochondrial amidoxime-reducing component 1) protect carriers from metabolic dysfunction-associated steatohepatitis (MASH) and cirrhosis. MTARC1 encodes the mARC1 enzyme, which is localized to the mitochondria and has no known MASH-relevant molecular function. Our studies aimed to expand on the published human genetic mARC1 data and to observe the molecular effects of mARC1 modulation in preclinical MASH models. METHODS AND RESULTS: We identified a novel human structural variant deletion in MTARC1, which is associated with various biomarkers of liver health, including alanine aminotransferase levels. Phenome-wide Mendelian Randomization analyses additionally identified novel putatively causal associations between MTARC1 expression, and esophageal varices and cardiorespiratory traits. We observed that protective MTARC1 variants decreased protein accumulation in in vitro overexpression systems and used genetic tools to study mARC1 depletion in relevant human and mouse systems. Hepatocyte mARC1 knockdown in murine MASH models reduced body weight, liver steatosis, oxidative stress, cell death, and fibrogenesis markers. mARC1 siRNA treatment and overexpression modulated lipid accumulation and cell death consistently in primary human hepatocytes, hepatocyte cell lines, and primary human adipocytes. mARC1 depletion affected the accumulation of distinct lipid species and the expression of inflammatory and mitochondrial pathway genes/proteins in both in vitro and in vivo models. CONCLUSIONS: Depleting hepatocyte mARC1 improved metabolic dysfunction-associated steatotic liver disease-related outcomes. Given the functional role of mARC1 in human adipocyte lipid accumulation, systemic targeting of mARC1 should be considered when designing mARC1 therapies. Our data point to plasma lipid biomarkers predictive of mARC1 abundance, such as Ceramide 22:1. We propose future areas of study to describe the precise molecular function of mARC1, including lipid trafficking and subcellular location within or around the mitochondria and endoplasmic reticulum.

Purification of time-resolved insulin granules reveals proteomic and lipidomic changes during granule aging.

Endocrine cells employ regulated exocytosis of secretory granules to secrete hormones and neurotransmitters. Secretory granule exocytosis depends on spatiotemporal variables such as proximity to the plasma membrane and age, with newly generated granules being preferentially released. Despite recent advances, we lack a comprehensive view of the molecular composition of insulin granules and associated changes over their lifetime. Here, we report a strategy for the purification of insulin secretory granules of distinct age from insulinoma INS-1 cells. Tagging the granule-resident protein phogrin with a cleavable CLIP tag, we obtain intact fractions of age-distinct granules for proteomic and lipidomic analyses. We find that the lipid composition changes over time, along with the physical properties of the membrane, and that kinesin-1 heavy chain (KIF5b) as well as Ras-related protein 3a (RAB3a) associate preferentially with younger granules. Further, we identify the Rho GTPase-activating protein (ARHGAP1) as a cytosolic factor associated with insulin granules.

The social and structural architecture of the yeast protein interactome.

Cellular functions are mediated by protein-protein interactions, and mapping the interactome provides fundamental insights into biological systems. Affinity purification coupled to mass spectrometry is an ideal tool for such mapping, but it has been difficult to identify low copy number complexes, membrane complexes and complexes that are disrupted by protein tagging. As a result, our current knowledge of the interactome is far from complete, and assessing the reliability of reported interactions is challenging. Here we develop a sensitive high-throughput method using highly reproducible affinity enrichment coupled to mass spectrometry combined with a quantitative two-dimensional analysis strategy to comprehensively map the interactome of Saccharomyces cerevisiae. Thousand-fold reduced volumes in 96-well format enabled replicate analysis of the endogenous GFP-tagged library covering the entire expressed yeast proteome1. The 4,159 pull-downs generated a highly structured network of 3,927 proteins connected by 31,004 interactions, doubling the number of proteins and tripling the number of reliable interactions compared with existing interactome maps2. This includes very-low-abundance epigenetic complexes, organellar membrane complexes and non-taggable complexes inferred by abundance correlation. This nearly saturated interactome reveals that the vast majority of yeast proteins are highly connected, with an average of 16 interactors. Similar to social networks between humans, the average shortest distance between proteins is 4.2 interactions. AlphaFold-Multimer provided novel insights into the functional roles of previously uncharacterized proteins in complexes. Our web portal ( www.yeast-interactome.org ) enables extensive exploration of the interactome dataset.

Genetically encoded multimeric tags for subcellular protein localization in cryo-EM.

Cryo-electron tomography (cryo-ET) allows for label-free high-resolution imaging of macromolecular assemblies in their native cellular context. However, the localization of macromolecules of interest in tomographic volumes can be challenging. Here we present a ligand-inducible labeling strategy for intracellular proteins based on fluorescent, 25-nm-sized, genetically encoded multimeric particles (GEMs). The particles exhibit recognizable structural signatures, enabling their automated detection in cryo-ET data by convolutional neural networks. The coupling of GEMs to green fluorescent protein-tagged macromolecules of interest is triggered by addition of a small-molecule ligand, allowing for time-controlled labeling to minimize disturbance to native protein function. We demonstrate the applicability of GEMs for subcellular-level localization of endogenous and overexpressed proteins across different organelles in human cells using cryo-correlative fluorescence and cryo-ET imaging. We describe means for quantifying labeling specificity and efficiency, and for systematic optimization for rare and abundant protein targets, with emphasis on assessing the potential effects of labeling on protein function.

A Review of Approaches for Mitigating Effects from Variable Operational Environments on Piezoelectric Transducers for Long-Term Structural Health Monitoring.

Extending the service life of ageing infrastructure, transportation structures, and processing and manufacturing plants in an era of limited resources has spurred extensive research and development in structural health monitoring systems and their integration. Even though piezoelectric transducers are not the only sensor technology for SHM, they are widely used for data acquisition from, e.g., wave-based or vibrational non-destructive test methods such as ultrasonic guided waves, acoustic emission, electromechanical impedance, vibration monitoring or modal analysis, but also provide electric power via local energy harvesting for equipment operation. Operational environments include mechanical loads, e.g., stress induced deformations and vibrations, but also stochastic events, such as impact of foreign objects, temperature and humidity changes (e.g., daily and seasonal or process-dependent), and electromagnetic interference. All operator actions, correct or erroneous, as well as unintentional interference by unauthorized people, vandalism, or even cyber-attacks, may affect the performance of the transducers. In nuclear power plants, as well as in aerospace, structures and health monitoring systems are exposed to high-energy electromagnetic or particle radiation or (micro-)meteorite impact. Even if environmental effects are not detrimental for the transducers, they may induce large amounts of non-relevant signals, i.e., coming from sources not related to changes in structural integrity. Selected issues discussed comprise the durability of piezoelectric transducers, and of their coupling and mounting, but also detection and elimination of non-relevant signals and signal de-noising. For long-term service, developing concepts for maintenance and repair, or designing robust or redundant SHM systems, are of importance for the reliable long-term operation of transducers for structural health monitoring.

Robust dimethyl-based multiplex-DIA doubles single-cell proteome depth via a reference channel.

Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data-independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single-cell samples, without losing proteomic depth. Lys-N digestion enables five-plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven-fold for microdissection and four-fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology.

Spatial proteomics in three-dimensional intact specimens.

Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of large biological specimens imaged in 3D are lacking. Here, we present DISCO-MS, a technology that combines whole-organ/whole-organism clearing and imaging, deep-learning-based image analysis, robotic tissue extraction, and ultra-high-sensitivity mass spectrometry. DISCO-MS yielded proteome data indistinguishable from uncleared samples in both rodent and human tissues. We used DISCO-MS to investigate microglia activation along axonal tracts after brain injury and characterized early- and late-stage individual amyloid-beta plaques in a mouse model of Alzheimer's disease. DISCO-bot robotic sample extraction enabled us to study the regional heterogeneity of immune cells in intact mouse bodies and aortic plaques in a complete human heart. DISCO-MS enables unbiased proteome analysis of preclinical and clinical tissues after unbiased imaging of entire specimens in 3D, identifying diagnostic and therapeutic opportunities for complex diseases. VIDEO ABSTRACT.

Rapid and In-Depth Coverage of the (Phospho-)Proteome With Deep Libraries and Optimal Window Design for dia-PASEF.

Data-independent acquisition (DIA) methods have become increasingly attractive in mass spectrometry-based proteomics because they enable high data completeness and a wide dynamic range. Recently, we combined DIA with parallel accumulation-serial fragmentation (dia-PASEF) on a Bruker trapped ion mobility (IM) separated quadrupole time-of-flight mass spectrometer. This requires alignment of the IM separation with the downstream mass selective quadrupole, leading to a more complex scheme for dia-PASEF window placement compared with DIA. To achieve high data completeness and deep proteome coverage, here we employ variable isolation windows that are placed optimally depending on precursor density in the m/z and IM plane. This is implemented in the freely available py_diAID (Python package for DIA with an automated isolation design) package. In combination with in-depth project-specific proteomics libraries and the Evosep liquid chromatography system, we reproducibly identified over 7700 proteins in a human cancer cell line in 44 min with quadruplicate single-shot injections at high sensitivity. Even at a throughput of 100 samples per day (11 min liquid chromatography gradients), we consistently quantified more than 6000 proteins in mammalian cell lysates by injecting four replicates. We found that optimal dia-PASEF window placement facilitates in-depth phosphoproteomics with very high sensitivity, quantifying more than 35,000 phosphosites in a human cancer cell line stimulated with an epidermal growth factor in triplicate 21 min runs. This covers a substantial part of the regulated phosphoproteome with high sensitivity, opening up for extensive systems-biological studies.

Unbiased spatial proteomics with single-cell resolution in tissues.

Mass spectrometry (MS)-based proteomics has become a powerful technology to quantify the entire complement of proteins in cells or tissues. Here, we review challenges and recent advances in the LC-MS-based analysis of minute protein amounts, down to the level of single cells. Application of this technology revealed that single-cell transcriptomes are dominated by stochastic noise due to the very low number of transcripts per cell, whereas the single-cell proteome appears to be complete. The spatial organization of cells in tissues can be studied by emerging technologies, including multiplexed imaging and spatial transcriptomics, which can now be combined with ultra-sensitive proteomics. Combined with high-content imaging, artificial intelligence and single-cell laser microdissection, MS-based proteomics provides an unbiased molecular readout close to the functional level. Potential applications range from basic biological questions to precision medicine.

Deep Visual Proteomics defines single-cell identity and heterogeneity.

Despite the availabilty of imaging-based and mass-spectrometry-based methods for spatial proteomics, a key challenge remains connecting images with single-cell-resolution protein abundance measurements. Here, we introduce Deep Visual Proteomics (DVP), which combines artificial-intelligence-driven image analysis of cellular phenotypes with automated single-cell or single-nucleus laser microdissection and ultra-high-sensitivity mass spectrometry. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. By individually excising nuclei from cell culture, we classified distinct cell states with proteomic profiles defined by known and uncharacterized proteins. In an archived primary melanoma tissue, DVP identified spatially resolved proteome changes as normal melanocytes transition to fully invasive melanoma, revealing pathways that change in a spatial manner as cancer progresses, such as mRNA splicing dysregulation in metastatic vertical growth that coincides with reduced interferon signaling and antigen presentation. The ability of DVP to retain precise spatial proteomic information in the tissue context has implications for the molecular profiling of clinical samples.

OpenCell: Endogenous tagging for the cartography of human cellular organization.

Elucidating the wiring diagram of the human cell is a central goal of the postgenomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry, and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery. We found that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions, and that RNA binding proteins form a specific subgroup defined by unique interaction and localization properties. Paired with a fully interactive website (opencell.czbiohub.org), our work constitutes a resource for the quantitative cartography of human cellular organization.

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

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

Reticulon-like REEP4 at the inner nuclear membrane promotes nuclear pore complex formation.

Nuclear pore complexes (NPCs) are channels within the nuclear envelope that mediate nucleocytoplasmic transport. NPCs form within the closed nuclear envelope during interphase or assemble concomitantly with nuclear envelope reformation in late stages of mitosis. Both interphase and mitotic NPC biogenesis require coordination of protein complex assembly and membrane deformation. During early stages of mitotic NPC assembly, a seed for new NPCs is established on chromatin, yet the factors connecting the NPC seed to the membrane of the forming nuclear envelope are unknown. Here, we report that the reticulon homology domain protein REEP4 not only localizes to high-curvature membrane of the cytoplasmic endoplasmic reticulum but is also recruited to the inner nuclear membrane by the NPC biogenesis factor ELYS. This ELYS-recruited pool of REEP4 promotes NPC assembly and appears to be particularly important for NPC formation during mitosis. These findings suggest a role for REEP4 in coordinating nuclear envelope reformation with mitotic NPC biogenesis.

AlphaMap: an open-source Python package for the visual annotation of proteomics data with sequence-specific knowledge.

SUMMARY: Integrating experimental information across proteomic datasets with the wealth of publicly available sequence annotations is a crucial part in many proteomic studies that currently lacks an automated analysis platform. Here, we present AlphaMap, a Python package that facilitates the visual exploration of peptide-level proteomics data. Identified peptides and post-translational modifications in proteomic datasets are mapped to their corresponding protein sequence and visualized together with prior knowledge from UniProt and with expected proteolytic cleavage sites. The functionality of AlphaMap can be accessed via an intuitive graphical user interface or-more flexibly-as a Python package that allows its integration into common analysis workflows for data visualization. AlphaMap produces publication-quality illustrations and can easily be customized to address a given research question. AVAILABILITY AND IMPLEMENTATION: AlphaMap is implemented in Python and released under an Apache license. The source code and one-click installers are freely available at https://github.com/MannLabs/alphamap. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In-Service Delaminations in FRP Structures under Operational Loading Conditions: Are Current Fracture Testing and Analysis on Coupons Sufficient for Capturing the Essential Effects for Reliable Predictions?

Quasi-static or cyclic loading of an artificial starter crack in unidirectionally fibre-reinforced composite test coupons yields fracture mechanics data-the toughness or strain-energy release rate (labelled G)-for characterising delamination initiation and propagation. Thus far, the reproducibility of these tests is typically between 10 and 20%. However, differences in the size and possibly the shape, but also in the fibre lay-up, between test coupons and components or structures raise additional questions: Is G from a coupon test a suitable parameter for describing the behaviour of delaminations in composite structures? Can planar, two-dimensional, delamination propagation in composite plates or shells be properly predicted from essentially one-dimensional propagation in coupons? How does fibre bridging in unidirectionally reinforced test coupons relate to delamination propagation in multidirectional lay-ups of components and structures? How can multiple, localised delaminations-often created by impact in composite structures-and their interaction under service loads with constant or variable amplitudes be accounted for? Does planar delamination propagation depend on laminate thickness, thickness variation or the overall shape of the structure? How does exposure to different, variable service environments affect delamination initiation and propagation? Is the microscopic and mesoscopic morphology of FRP composite structures sufficiently understood for accurate predictive modelling and simulation of delamination behaviour? This contribution will examine selected issues and discuss the consequences for test development and analysis. The discussion indicates that current coupon testing and analysis are unlikely to provide the data for reliable long-term predictions of delamination behaviour in FRP composite structures. The attempts to make the building block design methodology for composite structures more efficient via combinations of experiments and related modelling look promising, but models require input data with low scatter and, even more importantly, insight into the physics of the microscopic damage processes yielding delamination initiation and propagation.

Multi-omics profiling of living human pancreatic islet donors reveals heterogeneous beta cell trajectories towards type 2 diabetes.

Most research on human pancreatic islets is conducted on samples obtained from normoglycaemic or diseased brain-dead donors and thus cannot accurately describe the molecular changes of pancreatic islet beta cells as they progress towards a state of deficient insulin secretion in type 2 diabetes (T2D). Here, we conduct a comprehensive multi-omics analysis of pancreatic islets obtained from metabolically profiled pancreatectomized living human donors stratified along the glycemic continuum, from normoglycemia to T2D. We find that islet pools isolated from surgical samples by laser-capture microdissection display remarkably more heterogeneous transcriptomic and proteomic profiles in patients with diabetes than in non-diabetic controls. The differential regulation of islet gene expression is already observed in prediabetic individuals with impaired glucose tolerance. Our findings demonstrate a progressive, but disharmonic, remodelling of mature beta cells, challenging current hypotheses of linear trajectories toward precursor or transdifferentiation stages in T2D. Furthermore, through integration of islet transcriptomics with preoperative blood plasma lipidomics, we define the relative importance of gene coexpression modules and lipids that are positively or negatively associated with HbA1c levels, pointing to potential prognostic markers.

Quantification Approaches for Fatigue Crack Resistance of Thermoplastic Tape Layered Composites with Multiple Delaminations.

Automated tape placement with in-situ consolidation (ATPisc) is a layer-wise manufacturing process in which the achievement of proper interlayer bonding constitutes one of the most challenging aspects. In the present study, unidirectional carbon fiber reinforced thermoplastic laminates were produced following different manufacturing protocols using ATPisc. The interlayer bonding of the laminates produced was characterized by mode I fatigue fracture tests with double cantilever beam (DCB) specimens. Independent of the manufacturing approach, the laminates exhibited multiple cracking during DCB testing, which could not be evaluated simply following standard methods. Thus, various data analysis methodologies from literature were applied for the quantitative assessment of the fracture behavior of the laminate. The examination of the evolution of the damage parameter φ and the effective flexural modulus throughout testing enabled a better understanding of the damage accumulation. The Hartman-Schijve based approach was revealed to be a convenient method to present fatigue crack growth curves of laminates with multiple delaminations. Moreover, a preliminary attempt was made to employ a 'zero-fiber bridging' methodology to eliminate the effect of additional damage processes on the fatigue crack growth that resulted in large-scale, partially massive fiber bridging.

Magnetic domains and domain wall pinning in atomically thin CrBr3 revealed by nanoscale imaging.

The emergence of atomically thin van der Waals magnets provides a new platform for the studies of two-dimensional magnetism and its applications. However, the widely used measurement methods in recent studies cannot provide quantitative information of the magnetization nor achieve nanoscale spatial resolution. These capabilities are essential to explore the rich properties of magnetic domains and spin textures. Here, we employ cryogenic scanning magnetometry using a single-electron spin of a nitrogen-vacancy center in a diamond probe to unambiguously prove the existence of magnetic domains and study their dynamics in atomically thin CrBr3. By controlling the magnetic domain evolution as a function of magnetic field, we find that the pinning effect is a dominant coercivity mechanism and determine the magnetization of a CrBr3 bilayer to be about 26 Bohr magnetons per square nanometer. The high spatial resolution of this technique enables imaging of magnetic domains and allows to locate the sites of defects that pin the domain walls and nucleate the reverse domains. Our work highlights scanning nitrogen-vacancy center magnetometry as a quantitative probe to explore nanoscale features in two-dimensional magnets.