Cellular Scale Research Articles

Unveiling tryptophan dynamics and functions across model organisms via quantitative imaging.

Tryptophan is an essential amino acid involved in critical cellular processes in vertebrates, serving as a precursor for serotonin and kynurenine, which are key neuromodulators to influence neural and immune functions. Systematic and quantitative measurement of tryptophan is vital to understanding these processes. Here, we utilized a robust and highly responsive green ratiometric indicator for tryptophan (GRIT) to quantitatively measure tryptophan dynamics in bacteria, mitochondria of mammalian cell cultures, human serum, and intact zebrafish. At the cellular scale, these quantitative analyses uncovered differences in tryptophan dynamics across cell types and organelles. At the whole-organism scale, we revealed that inflammation-induced tryptophan concentration increases in zebrafish brain led to elevated serotonin and kynurenine levels, prolonged sleep duration, suggesting a novel metabolic connection between immune response and behavior. Moreover, GRIT's application in detecting reduced serum tryptophan levels in patients with inflammation symptoms suggests its potential as a high-throughput diagnostic tool. In summary, this study introduces GRIT as a powerful method for studying tryptophan metabolism and its broader physiological implications, paving the way for new insights into the metabolic regulation of health and disease across multiple biological scales.

AI-powered simulation-based inference of a genuinely spatial-stochastic gene regulation model of early mouse embryogenesis.

Understanding how multicellular organisms reliably orchestrate cell-fate decisions is a central challenge in developmental biology, particularly in early mammalian development, where tissue-level differentiation arises from seemingly cell-autonomous mechanisms. In this study, we present a multi-scale, spatial-stochastic simulation framework for mouse embryogenesis, focusing on inner cell mass (ICM) differentiation into epiblast (EPI) and primitive endoderm (PRE) at the blastocyst stage. Our framework models key regulatory and tissue-scale interactions in a biophysically realistic fashion, capturing the inherent stochasticity of intracellular gene expression and intercellular signaling, while efficiently simulating these processes by advancing event-driven simulation techniques. Leveraging the power of Simulation-Based Inference (SBI) through the AI-driven Sequential Neural Posterior Estimation (SNPE) algorithm, we conduct a large-scale Bayesian inferential analysis to identify parameter sets that faithfully reproduce experimentally observed features of ICM specification. Our results reveal mechanistic insights into how the combined action of autocrine and paracrine FGF4 signaling coordinates stochastic gene expression at the cellular scale to achieve robust and reproducible ICM patterning at the tissue scale. We further demonstrate that the ICM exhibits a specific time window of sensitivity to exogenous FGF4, enabling lineage proportions to be adjusted based on timing and dosage, thereby extending current experimental findings and providing quantitative predictions for both mutant and wild-type ICM systems. Notably, FGF4 signaling not only ensures correct EPI-PRE lineage proportions but also enhances ICM resilience to perturbations, reducing fate-proportioning errors by 10-20% compared to a purely cell-autonomous system. Additionally, we uncover a surprising role for variability in intracellular initial conditions, showing that high gene-expression heterogeneity can improve both the accuracy and precision of cell-fate proportioning, which remains robust when fewer than 25% of the ICM population experiences perturbed initial conditions. Our work offers a comprehensive, spatial-stochastic description of the biochemical processes driving ICM differentiation and identifies the necessary conditions for its robust unfolding. It also provides a framework for future exploration of similar spatial-stochastic systems in developmental biology.

Rupture strength of living cell monolayers

To fulfil their function, epithelial tissues need to sustain mechanical stresses and avoid rupture. Although rupture is usually undesired, it is central to some developmental processes, for example, blastocoel formation. Nonetheless, little is known about tissue rupture because it is a multiscale phenomenon that necessitates comprehension of the interplay between mechanical forces and biological processes at the molecular and cellular scales. Here we characterize rupture in epithelial monolayers using mechanical measurements, live imaging and computational modelling. We show that despite consisting of only a single layer of cells, monolayers can withstand surprisingly large deformations, often accommodating several-fold increases in their length before rupture. At large deformation, epithelia increase their stiffness multiple fold in a process controlled by a supracellular network of keratin filaments. Perturbing the keratin network organization fragilized the monolayers and prevented strain-stiffening. Although the kinetics of adhesive bond rupture ultimately control tissue strength, tissue rheology and the history of deformation set the strain and stress at the onset of fracture.

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine.

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.

Multi-Modal Investigation of Metabolism in Murine Breast Cancer Cell Lines Using Fluorescence Lifetime Microscopy and Hyperpolarized 13C-Pyruvate Magnetic Resonance Spectroscopy.

Background/Objectives: Despite the role of metabolism in breast cancer metastasis, we still cannot predict which breast tumors will progress to distal metastatic lesions or remain dormant. This work uses metabolic imaging to study breast cancer cell lines (4T1, 4T07, and 67NR) with differing metastatic potential in a 3D collagen gel bioreactor system. Methods: Within the bioreactor, hyperpolarized magnetic resonance spectroscopy (HP-MRS) is used to image lactate/pyruvate ratios, while fluorescence lifetime imaging microscopy (FLIM) of endogenous metabolites measures metabolism at the cellular scale. Results: HP-MRS results showed no lactate peak for 67NR and a comparatively large lactate/pyruvate ratio for both 4T1 and 4T07 cell lines, suggestive of greater pyruvate utilization with greater metastatic potential. Similar patterns were observed using FLIM with significant increases in FAD intensity, redox ratio, and NAD(P)H lifetime. The lactate/pyruvate ratio was strongly correlated to NAD(P)H lifetime, consistent with the role of NADH as an electron donor for the glycolytic pathway, suggestive of an overall upregulation of metabolism (both glycolytic and oxidative), for the 4T07 and 4T1 cell lines compared to the non-metastatic 67NR cell line. Conclusions: These findings support a complementary role for HP-MRS and FLIM enabled by a novel collagen gel bioreactor system to investigate metastatic potential and cancer metabolism.

Application of phase space file secondary computation method in cell dose distribution

Phase space files can store the particle information in one or more planes of radiation particles simulated by the Monte Carlo (MC) method. The secondary calculation method based on phase space files is commonly used to improve the efficiency of MC simulations. However, it is still unclear whether phase space files are applicable for microdosimetric evaluations. In this study, voxel-type and mesh-type monolayer cell population models of different sizes were constructed, and phase space files of secondary electrons generated by photons with different initial energies were obtained using the MC software -- PHITS. The overall average dose caused by the secondary electron phase space files in the region of interest and their microdosimetric distribution within cells were calculated and compared with the results caused by the initial photons under the same geometric conditions. The results showed that the adoption of secondary electron phase space files had almost no impact on the evaluation of macroscopic average dose, with deviations lower than 3% compared to the overall dose caused by the initial photons in the Petri dish. For microdosimetric distributions of the voxel-type model and the two different morphologies of mesh-type cell models, with a macroscopic accumulated dose of 1 mGy, the relative deviation of the cell dose distribution generated by the initial photons and the phase space files was below 10% and the total computation time of phase space files was below 2% of initial photon's. For accumulated doses of 10, 50, and 100 mGy, the relative deviation of the cell nucleus specific energy obtained by secondary electrons and initial photons was greater than 10%. As the size of the culture dish increased, the differences in cell dose distributions also increased, with the root mean square error (RMSE) and coefficient of variation (Cv) of dose distributions both exceeding 30%. In conclusion, this study assessed the effectiveness of the secondary calculation method utilizing phase space files for dose evaluation at the cellular scale. This research offers essential technical support and theoretical foundations for the utilization of this approach in microdosimetric investigations at the cellular level.

Thermostatted Kinetic Theory Structures in Biophysics: Generalizations and Perspectives

The mathematical modeling of multicellular systems is an important branch of biophysics, which focuses on how the system properties emerge from the elementary interaction between the constituent elements. Recently, mathematical structures have been proposed within the thermostatted kinetic theory for the modeling of complex living systems and have been profitably employed for the modeling of various complex biological systems at the cellular scale. This paper deals with a class of generalized thermostatted kinetic theory frameworks that can stand in as background paradigms for the derivation of specific models in biophysics. Specifically, the fundamental homogeneous thermostatted kinetic theory structures of the recent literature are recovered and generalized in order to take into consideration further phenomena in biology. The generalizations concern the conservative, the nonconservative, and the mutative interactions between the inner system and the outer environment. In order to sustain the strength of the new structures, some specific models of the literature are reset into the style of the new frameworks of the thermostatted kinetic theory. The selected models deal with breast cancer, genetic mutations, immune system response, and skin fibrosis. Future research directions from the theoretical and modeling viewpoints are discussed in the whole paper and are mainly devoted to the well-posedness in the Hadamard sense of the related initial boundary value problems, to the spatial–velocity dynamics and to the derivation of macroscopic-scale dynamics.

The endoplasmic reticulum as an active liquid network

The peripheral endoplasmic reticulum (ER) forms a dense, interconnected, and constantly evolving network of membrane-bound tubules in eukaryotic cells. While individual structural elements and the morphogens that stabilize them have been described, a quantitative understanding of the dynamic large-scale network topology remains elusive. We develop a physical model of the ER as an active liquid network, governed by a balance of tension-driven shrinking and new tubule growth. This minimalist model gives rise to steady-state network structures with density and rearrangement timescales predicted from the junction mobility and tubule spawning rate. Several parameter-independent geometric features of the liquid network model are shown to be representative of ER architecture in live mammalian cells. The liquid network model connects the timescales of distinct dynamic features such as ring closure and new tubule growth in the ER. Furthermore, it demonstrates how the steady-state network morphology on a cellular scale arises from the balance of microscopic dynamic rearrangements.

Spatiotemporal Modeling of Mitochondrial Network Architecture

In many cell types, mitochondria undergo extensive fusion and fission to form dynamic, responsive network structures that contribute to a number of homeostatic, metabolic, and signaling functions. The relationship between the dynamic interactions of individual mitochondrial units and the cell-scale network architecture remains an open area of study. In this work, we use coarse-grained simulations and approximate analytic models to establish how the network morphology is governed by local mechanical and kinetic parameters. The transition between fragmented structures and extensive networks is controlled by local fusion-to-fission ratios, network density, and geometric constraints. Similar fusion rate constants are found to account for the very different structures formed by mammalian networks (poised at the percolation transition) and well-connected budding yeast networks. Over a broad parameter range, the simulated network structures can be described by effective mean-field association constants that exhibit a nonlinear dependence on the microscopic nonequilibrium fusion, fission, and transport rates. Intermediate fusion rate constants are shown to result in the highest rates of network remodeling, with mammalian mitochondrial networks situated in a regime of high turnover. This spatially resolved modeling and simulation framework helps elucidate the emergence of cellular scale network structures, and allows for the quantitative extraction of microscopic kinetic parameters from past and future experimental data. Published by the American Physical Society 2024

Molecular regulation of reversible cardiac remodeling: lessons from species with extreme physiological adaptations.

Some vertebrates evolved to have a remarkable capacity for anatomical and physiological plasticity in response to environmental challenges. One example of such plasticity can be found in the ambush-hunting snakes of the genus Python, which exhibit reversible cardiac growth with feeding. The predation strategy employed by pythons is associated with months-long fasts that are arrested by ingestion of large prey. Consequently, digestion compels a dramatic increase in metabolic rate and hypertrophy of multiple organs, including the heart. In this Review, we summarize the post-prandial cardiac adaptations in pythons at the whole-heart, cellular and molecular scales. We highlight circulating factors and cellular signaling pathways that are altered during digestion to affect cardiac form and function and propose possible mechanisms that may drive the post-digestion regression of cardiac mass. Adaptive physiological cardiac hypertrophy has also been observed in other vertebrates, including in fish acclimated to cold water, birds flying at high altitudes and exercising mammals. To reveal potential evolutionarily conserved features, we summarize the molecular signatures of reversible cardiac remodeling identified in these species and compare them with those of pythons. Finally, we offer a perspective on the potential of biomimetics targeting the natural biology of pythons as therapeutics for human heart disease.

Length-scale study in deep learning prediction for non-small cell lung cancer brain metastasis

Deep learning-assisted digital pathology has demonstrated the potential to profoundly impact clinical practice, even surpassing human pathologists in performance. However, as deep neural network (DNN) architectures grow in size and complexity, their explainability decreases, posing challenges in interpreting pathology features for broader clinical insights into physiological diseases. To better assess the interpretability of digital microscopic images and guide future microscopic system design, we developed a novel method to study the predictive feature length-scale that underpins a DNN’s predictive power. We applied this method to analyze a DNN’s capability in predicting brain metastasis from early-stage non-small-cell lung cancer biopsy slides. This study quantifies DNN’s attention for brain metastasis prediction, targeting features at both the cellular scale and tissue scale in H&E-stained histological whole slide images. At the cellular scale, the predictive power of DNNs progressively increases with higher resolution and significantly decreases when the resolvable feature length exceeds 5 microns. Additionally, DNN uses more macro-scale features associated with tissue architecture and is optimized when assessing visual fields greater than 41 microns. Our study computes the length-scale requirements for optimal DNN learning on digital whole-slide microscopic images, holding the promise to guide future optical microscope designs in pathology applications and facilitating downstream deep learning analysis.

Abstract C016: Optical Techniques to Democratize Intravital Quantification of Metabolic Heterogeneity

Abstract Since metabolic and vascular adaptations are essential for tumor cell survival, a technique capable of tracking metabolic heterogeneity at both the bulk tumor and cellular scales over extended timelines is necessary. To address this technological need, we have developed a multi-scale optical microscope relying on exogenous fluorescent reporters for dynamic spatiotemporal imaging of major metabolic pathways in vivo. This microscope utilizes a low-cost CMOS detector and LEDs for excitation to enable fluorescence microscopy in resource-limited settings. Here, we implement our system to assess changes in bulk tumor and cellular metabolic heterogeneity as vasculature is perturbed. 4T1 breast tumor-bearing mice were treated with combretastatin A-1 (a vascular disruptor), implanted with window chambers, and temporally imaged (n=5/group). Fluorescence imaging and Gabor filter/Djikstra segmentation approaches enabled metabolic and vascular analyses, respectively. We demonstrated the system’s ability to acquire co-registered images of vasculature, morphology, and distinct metabolic endpoints reporting on glucose uptake, fatty acid uptake, and mitochondrial metabolism at both wide-field and high resolutions. At 12 hours post-treatment, the tumors’ metabolism shifted from mitochondrial respiration to glycolysis in avascular regions. Further, we report a higher average vascular density in untreated versus treated mice at 108 hours post-treatment (p<0.05, Kolmogorov-Smirnov test). On exploring the relationship between metabolic activity and vasculature, regions with high local tortuosity in the untreated group exhibited a higher reliance on fatty acid uptake than high tortuosity regions in the treatment group, indicating these cells’ reliance on fatty acids within a complex vascular network. Our multiplexed imaging strategy enables the quantification of tumor metabolism and vasculature over multiple length scales, which could be leveraged to study adaptations following clinical therapies. Citation Format: Enakshi Sunassee. Optical Techniques to Democratize Intravital Quantification of Metabolic Heterogeneity [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C016.

A versatile bioluminescent probe with tunable color.

Bioluminescence is a powerful method for imaging in vivo, but applications at the microscale are far from routine. This is due, in part, to a lack of versatile tools for visualizing dynamic events. To address this void, we developed a new platform-Bioluminescence Resonance Energy mAKe over with a Fluorescence-Activating absorption-Shifting Tag (BREAKFAST). BREAKFAST features a bright luciferase combined with a chemogenetic tag (pFAST) for rapid color switching. In the presence of luciferin and a discrete fluorogenic ligand, signal is observed via resonance energy transfer. We evaluated spectral outputs with various fluorogens and established the utility of BREAKFAST for combined fluorescence and bioluminescence imaging. Dynamic, four-color visualization was achieved with sequential ligand addition and spectral phasor analysis. We further showed selective signal quenching with a dark fluorogen. Collectively, this work establishes a new method for bioluminescence imaging at the cellular scale and sets the stage for continued probe development.

In Vivo Visualization of Intravascular Patrolling Immune Cells in the Primate Eye.

Insight into the immune status of the living eye is essential as we seek to understand ocular disease and develop new treatments. The nonhuman primate (NHP) is the gold standard preclinical model for therapeutic development in ophthalmology, owing to the similar visual system and immune landscape in the NHP relative to the human. Here, we demonstrate the utility of phase-contrast adaptive optics scanning light ophthalmoscope (AOSLO) to visualize immune cell dynamics on the cellular scale, label-free in the NHP. Phase-contrast AOSLO was used to image preselected areas of retinal vasculature in five NHP eyes. Images were registered to correct for eye motion, temporally averaged, and analyzed for immune cell activity. Cell counts, dimensions, velocities, and frequency per vessel were determined manually and compared between retinal arterioles and venules. Based on cell appearance and circularity index, cells were divided into three morphologies: ovoid, semicircular, and flattened. Immune cells were observed migrating along vascular endothelium with and against blood flow. Cell velocity did not significantly differ between morphology or vessel type and was independent of blow flood. Venules had a significantly higher cell frequency than arterioles. A higher proportion of cells resembled "flattened" morphology in arterioles. Based on cell speeds, morphologies, and behaviors, we identified these cells as nonclassical patrolling monocytes (NCPMs). Phase-contrast AOSLO has the potential to reveal the once hidden behaviors of single immune cells in retinal circulation and can do so without the requirement of added contrast agents that may disrupt immune cell behavior.

Abstract A054: Collagen fiber architecture modulates PDAC cell and organoid phenotype

Abstract Introduction Collagen plays a significant role in the development of pancreatic ductal adenocarcinoma (PDAC). Disease progression is mediated by a dense collagenous stroma, which acts as a barrier to drug delivery and immune cell access. Current in vitro models do not well represent the collagenous stroma seen in PDAC. The aim of this study is to develop a fibrillar collagen-based hydrogel system which mimics the tumour microenvironment seen in PDAC and assess cell-mediated collagen remodeling. Methods Metastatic SUIT2-007 pancreatic cancer cells, non-metastatic BxPC3 pancreatic cancer cells, and human pancreatic stellate cells (hPSCs) were encapsulated in bovine type I collagen gels of various concentrations. KPC mouse derived tumour organoids were also encapsulated in bovine type I collagen gels of 2 mg/ml and 6 mg/ml concentrations, basement membrane extract (BME), and a combination of BME and collagen gel. Cell viability and proliferation was assessed using Live/Dead imaging and CellTitre-Glo 3D respectively. Cell mediated collagen remodeling was assessed via monitoring of gel contraction on the mm scale, and confocal reflectance microscopy on the cellular scale. Collagen contraction around cells was assessed by measuring the pixel intensity of collagen fibrils within the vicinity of the cell. Cell phenotype was assessed via immunofluorescent staining and qPCR. Results Collagen concentration alters the hydrogel structure and stiffness, with 3 mg/ml collagen resulting in straighter, shorter and thicker fibrils. Metastatic SUIT-007 cells contracted collagen gels more rapidly than non-metastatic BxPC3 cells, with both achieving similar contraction by day 7. SUIT2-007 cells remodel and align soft 1 mg/ml gels while 3 mg/ml gels proved to be sufficiently stiff to resist remodeling. Cellular collagen association was increased in more elongated SUIT-007 cells than in BxPC3 cells. Organoid proliferation varies with hydrogel collagen concentration. Conclusion Metastatic SUIT2-007 cells were able to remodel collagen more rapidly and to a greater extent than non-metastatic BxPC3 cells. Collagen supports an invasive organoid phenotype. Collagen gels combined with confocal reflectance microscopy provide a platform for the quantitative assessment of cell-matrix interactions in PDAC. Acknowledgements This project was funded by a Medical Traineeship from UCD School of Medicine, and summer student scholarships from Irish Cancer Society and Breakthrough Cancer Research. We acknowledge the Conway Imaging Core Facility for assistance with microscopy. Citation Format: Ciara L Doyle, Shanu Xavier, Jarren Y.S Lu, Leah Fallon, Anwesha Sarkar, Jan J Baguyo, Maela Gars, Stephen D Thorpe. Collagen fiber architecture modulates PDAC cell and organoid phenotype [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A054.

Examining and Supporting Mechanistic Explanations Across Chemistry and Biology Courses.

Causal mechanistic reasoning is a thinking strategy that can help students explain complex phenomena using core ideas commonly emphasized in separate undergraduate courses, as it requires students to identify underlying entities, unpack their relevant properties and interactions, and link them to construct mechanistic explanations. As a crossdisciplinary group of biologists, chemists, and teacher educators, we designed a scaffolded set of tasks that require content knowledge from biology and chemistry to construct nested hierarchical mechanistic explanations that span three scales (molecular, macromolecular, and cellular). We examined student explanations across seven introductory and upper-level biology and chemistry courses to determine how the construction of mechanistic explanations varied across courses and the relationship between the construction of mechanistic explanations at different scales. We found non-, partial, and complete mechanistic explanations in all courses and at each scale. Complete mechanistic explanation construction was lowest in introductory chemistry, about the same across biology and organic chemistry, and highest in biochemistry. Across tasks, the construction of a mechanistic explanation at a smaller scale was associated with constructing a mechanistic explanation for larger scales; however, the use of molecular scale disciplinary resources was only associated with complete mechanistic explanations at the macromolecular, not cellular scale.

Spatial landscapes of cancers: insights and opportunities.

Solid tumours comprise many different cell types organized in spatially structured arrangements, with substantial intratumour and intertumour heterogeneity. Advances in spatial profiling technologies over the past decade hold promise to capture the complexity of these cellular architectures to build a holistic view of the intricate molecular mechanisms that shape the tumour ecosystem. Some of these mechanisms act at the cellular scale and are controlled by cell-autonomous programmes or communication between nearby cells, whereas other mechanisms result from coordinated efforts between large networks of cells and extracellular molecules organized into tissues and organs. In this Review we provide insights into the application of single-cell and spatial profiling tools, with a focus on spatially resolved transcriptomic tools developed to understand the cellular architecture of the tumour microenvironment and identify opportunities to use them to improve clinical management of cancers.

Modeling Microtubule Dynamics on Lomonosov-2 Supercomputer of Moscow State University: from Atomistic to Cellular Scale Simulations

Modeling Microtubule Dynamics on Lomonosov-2 Supercomputer of Moscow State University: from Atomistic to Cellular Scale Simulations

Advanced multiparametric image spectroscopy and super-resolution microscopy reveal a minimal model of CD95 signal initiation.

Unraveling the concentration-dependent spatiotemporal organization of receptors in the plasma membrane is crucial to understand cell signal initiation. A paradigm of this process is the oligomerization of CD95 during apoptosis signaling, with different oligomerization models being discussed. Here, we establish the molecular-sensitive approach cell lifetime Förster resonance energy transfer image spectroscopy to determine CD95 configurations in live cells. These data are corroborated by stimulated emission depletion microscopy, confocal photobleaching step analysis, and fluorescence correlation spectroscopy. We probed CD95 interactions for concentrations of ~10 to 1000 molecules per square micrometer, over nanoseconds to hours, and molecular to cellular scales. Quantitative benchmarking was achieved establishing high-fidelity monomer and dimer controls. While CD95 alone is primarily monomeric (~96%) and dimeric (4%), the addition of ligand induces oligomerization to dimers/trimers (~15%) leading to cell death. This study highlights molecular concentration effects and oligomerization dynamics. It reveals a minimal model, where small CD95 oligomers suffice to efficiently initiate signaling.

Multiplex Profiling of miR-122 for Preclinical and Clinical Evaluation of Drug-Induced Liver Injury by a Full-Scale Platform.

Diagnostic and monitoring for drug-induced liver injury (DILI) predominantly rely on serum aminotransferases. However, owing to their widespread expression across multiple organs, a significant challenge emerges from the absence of reliable biomarkers for DILI diagnosis. Herein, we introduce a concept for DILI detection, circumventing the nonspecific elevation and delayed release of aminotransferases and then straightforwardly focusing on the core feature of DILI, abnormal gene expression caused by drug overdose. The developed full-scale platform integrates the properties of spherical nucleic acids with elaborately designed fluorescence in situ hybridization sequences, enabling the sensitive and specific profiling of drug-overdosed miR-122 expression alterations across molecular, cellular, organismal, and clinical scales and effectively bypassing the phenotypic features of disease. Furthermore, the diagnostic efficacies of serum and total RNA extracted from both mouse and human blood samples for DILI diagnosis were analyzed using the receiver operating characteristic curve and principal component analysis. We anticipate that this universal platform holds potential in facilitating DILI diagnosis, therapeutic evaluation, and prognosis.