Cellular Resolution Research Articles

Multiplexed siRNA Immunoassay Unveils Spatial and Quantitative Dimensions of siRNA Function, Abundance, and Localization In Vitro and In Vivo.

Small interfering RNAs (siRNAs) have been successfully used as therapeutics to silence disease-causing genes when conjugated to ligands or formulated in lipid nanoparticles to target relevant cell types for efficacy while sparing other cells for safety. To support the development of new methods for delivery of siRNA therapeutics, we developed and characterized a panel of antibodies generated against chemically modified nucleotides used in therapeutic siRNA molecules, identifying a monoclonal antibody that detects a broad range of siRNA representing distinct sequences and modification patterns. By integrating this anti-siRNA antibody with additional reagents, we created a multiplex siRNA immunoassay that simultaneously quantifies siRNA uptake, trafficking, and silencing activity. Using immunohistochemistry (IHC), we applied our method on tissues from mice treated with unconjugated, GalNAc-conjugated, or cholesterol-conjugated siRNAs and quantitatively assessed the biodistribution and activity of siRNAs in various organs. In addition, we used high-content imaging (HCI) and applied our multiplex siRNA immunoassay in tissue culture to enable simultaneous quantification of siRNA uptake, activity, and intracellular colocalization with endosome markers. These methods provide a robust platform for testing nucleic acid delivery methods in vitro and in vivo, allowing precise analysis and visualization of the pharmacokinetics and pharmacodynamics of siRNA therapeutics with cellular and subcellular resolution.

BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk.

While single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profiles of one sample or condition, and bulk profiles across multiple samples and conditions. To bridge this gap, we propose BuDDI (BUlk Deconvolution with Domain Invariance). BuDDI utilizes domain adaptation techniques to effectively integrate available corpora of case-control bulk and reference scRNA-seq observations to infer cell-type-specific perturbation effects. BuDDI achieves this by learning independent latent spaces within a single variational autoencoder (VAE) encompassing at least four sources of variability: 1) cell type proportion, 2) perturbation effect, 3) structured experimental variability, and 4) remaining variability. Since each latent space is encouraged to be independent, we simulate perturbation responses by independently composing each latent space to simulate cell-type-specific perturbation responses. We evaluated BuDDI's performance on simulated and real data with experimental designs of increasing complexity. We first validated that BuDDI could learn domain invariant latent spaces on data with matched samples across each source of variability. Then we validated that BuDDI could accurately predict cell-type-specific perturbation response when no single-cell perturbed profiles were used during training; instead, only bulk samples had both perturbed and non-perturbed observations. Finally, we validated BuDDI on predicting sex-specific differences, an experimental design where it is not possible to have matched samples. In each experiment, BuDDI outperformed all other comparative methods and baselines. As more reference atlases are completed, BuDDI provides a path to combine these resources with bulk-profiled treatment or disease signatures to study perturbations, sex differences, or other factors at single-cell resolution.

Impedance mapping with high-density microelectrode array chips reveals dynamic heterogeneity of in vitro epithelial barriers

Epithelial tissues in vitro undergo dynamic changes while differentiating heterogeneously on the culture substrate. This gives rise to diverse cellular arrangements which are undistinguished by conventional analysis approaches, such as transepithelial electrical resistance measurement or permeability assays. In this context, solid substrate-based systems with integrated electrodes and electrochemical impedance monitoring capability can address the limited spatiotemporal resolution of traditional porous membrane-based methods. This label-free technique facilitates local, continuous, long-term analysis of tissue barrier properties for organ-on-chip applications. Increasing spatial resolution requires small electrodes arranged in a dense array, known as high-density microelectrode arrays (HD-MEAs). Integrated with Complementary Metal Oxide Semiconductor (CMOS) technology for multiplexing and rapid impedance measurements, HD-MEAs can enable high spatiotemporal resolution assessments of epithelial tissues. Here, we used 16,384 CMOS-integrated HD-MEA chip with subcellular-sized electrodes (8 μm diameter, 15 μm pitch, patterned in 16 clusters each consisting of 1024 electrodes in a 32 × 32 matrix) and impedance sensing capability to monitor dynamic evolution of Caco-2 cells, such as their proliferation, barrier formation, and 3D structure development on the chip. Changes in impedance at the selected frequency of 1 kHz (|Z|1kHz) enabled monitoring and analyzing the life cycle of Caco-2 cells grown on the HD-MEA chips (up to + 453% change after 7 days in culture). The |Z|1kHz maps of proliferating Caco-2 cells and the differentiating epithelial tissue developing 3D domes aligned with the corresponding optical images at cellular resolution, which demonstrates the capability of the chip in tracking the dynamic heterogeneity of Caco-2 tissues in a label free and real-time fashion. Importantly, |Z|1kHz maps acquired during chemically induced barrier disruption showed electrodes covered with 3D cell domes experienced a stronger decrease in impedance than those covered with adherent cells (-41% ± sd 10% against -16% ± sd 10%, respectively). This method could thus, in principle, enable detection of tissue barrier disrupting and modifying agents with higher specificity. Epithelial barrier function assays benefit from using HD-MEA impedance sensors due to their increased informativity and resolution, which will be of great value in future organ-on-chip platforms.

A unified framework for cell-type-specific eQTL prioritization by integrating bulk and scRNA-seq data.

Genome-wide association studies (GWASs) have identified numerous genetic variants associated with complex traits, yet the biological interpretation remains challenging, especially for variants in non-coding regions. Expression quantitative trait locus (eQTL) studies have linked these variations to gene expression, aiding in identifying genes involved in disease mechanisms. Traditional eQTL analyses using bulk RNA sequencing (bulk RNA-seq) provide tissue-level insights but suffer from signal loss and distortion due to unaddressed cellular heterogeneity. Recently, single-cell RNA-seq (scRNA-seq) has provided higher resolution, enabling cell-type-specific eQTL (ct-eQTL) analyses. However, these studies are limited by their smaller sample sizes and technical constraints. In this paper, we present a statistical framework, IBSEP, which integrates bulk RNA-seq and scRNA-seq data for enhanced ct-eQTL prioritization. Our method employs a hierarchical linear model to combine summary statistics from both data types, overcoming the limitations while leveraging the advantages associated with each technique. Through extensive simulations and real data analyses, including peripheral blood mononuclear cells and brain cortex datasets, IBSEP demonstrated superior performance in identifying ct-eQTLs compared to existing methods. Our approach unveils transcriptional regulatory mechanisms specific to cell types, offering deeper insights into the genetic basis of complex diseases at a cellular resolution.

Probabilistic cellular automata simulation of microstructure evolution: the role of model parameters on precision and uncertainty

Abstract Probabilistic cellular automata (PCA) is a widely used and cost-efficient method for simulating microstructural evolution. In this method, probabilistic state change rules determine the evolution of cell states at each time step. However, its stochastic nature introduces inherent uncertainty, leading to non-repeatable results. Most microstructural simulation studies assess the accuracy of simulations by comparing predicted results with experimental observations, neglecting uncertainties in mathematical models and algorithms. In this study, the precision and stochastic behavior of microstructure evolution in the PCA simulations were investigated. The probabilities of transformations of cell states at each time step were formulated, and discrete probability distribution functions (dPDF) were introduced to analyze the frequency distribution of simulation outcomes. The performance and consistency of these dPDFs were assessed by comparing statistical analyzes of PCA simulation results with dPDF predictions, revealing that the variance of simulation results is less than that of the binomial distribution function. Additionally, the effects of modeling parameters, such as model size, cellular resolution, and probability distribution of state changes in two- and three-dimensional PCA modeling, on the precision and reliability of simulation results were studied. In PCA models, simulation uncertainty inversely relates to the square root of model size. Furthermore, in 2D simulations, uncertainty is inversely proportional to the square root of the cellular resolution, while in 3D simulations, it is inversely proportional to the cellular resolution itself. These findings provide a simple and computationally efficient method for evaluating PCA simulation uncertainty and determining optimal simulation parameters, including model size, cellular resolution, and dimensionality.

Cell-autonomous timing drives the vertebrate segmentation clock's wave pattern.

Rhythmic and sequential segmentation of the growing vertebrate body relies on the segmentation clock, a multi-cellular oscillating genetic network. The clock is visible as tissue-level kinematic waves of gene expression that travel through the presomitic mesoderm (PSM) and arrest at the position of each forming segment. Here, we test how this hallmark wave pattern is driven by culturing single maturing PSM cells. We compare their cell-autonomous oscillatory and arrest dynamics to those we observe in the embryo at cellular resolution, finding similarity in the relative slowing of oscillations and arrest in concert with differentiation. This shows that cell-extrinsic signals are not required by the cells to instruct the developmental program underlying the wave pattern. We show that a cell-autonomous timing activity initiates during cell exit from the tailbud, then runs down in the anterior-ward cell flow in the PSM, thereby using elapsed time to provide positional information to the clock. Exogenous FGF lengthens the duration of the cell-intrinsic timer, indicating extrinsic factors in the embryo may regulate the segmentation clock via the timer. In sum, our work suggests that a noisy cell-autonomous, intrinsic timer drives the slowing and arrest of oscillations underlying the wave pattern, while extrinsic factors in the embryo tune this timer's duration and precision. This is a new insight into the balance of cell-intrinsic and -extrinsic mechanisms driving tissue patterning in development.

Preclinical and Clinical-Scale Magnetic Particle Imaging of Natural Killer Cells: in vitro and ex vivo Demonstration of Cellular Sensitivity, Resolution, and Quantification.

Clinical adoption of NK cell immunotherapy is underway for medulloblastoma and osteosarcoma, however there is currently little feedback on cell fate after administration. We propose magnetic particle imaging (MPI) may have applications for the quantitative detection of NK cells. Human-derived NK-92 cells were labeled by co-incubation with iron oxide nanoparticles (VivoTrax™) for 24h then excess nanoparticles were washed with centrifugation. Cytolytic activity of labeled versus unlabeled NK-92 cells was assessed after 4h of co-incubation with medulloblastoma cells (DAOY) or osteosarcoma cells (LM7 or OS17). Labeled NK-92 cells at two different doses (0.5 or 1 × 106) were administered to excised mouse brains (cerebellum), fibulas, and lungs then imaged by 3D preclinical MPI (MOMENTUM™) for detection relative to fiducial markers. NK-92 cells were also imaged by clinical-scale MPI under development at Magnetic Insight Inc. NK-92 cells were labeled with an average of 3.17 pg Fe/cell with no measurable effects on cell viability or cytolytic activity against 3 tumor cell lines. MPI signal was directly quantitative with the number of labeled NK-92 cells, with preclinical limit of detection of 3.1 × 104 cells on MOMENTUM imager. Labeled NK-92 cells could be accurately localized in mouse brains, fibulas, and lungs within < 1mm of stereotactic injection coordinates with preclinical scanner. Feasibility for detection on a clinical-scale MPI scanner was demonstrated using 4 × 107 labeled NK-92 cells, which is in the range of NK cell doses administered in our previous clinical trial. MPI can provide sensitive, quantitative, and accurate spatial information on NK cells soon after delivery, showing initial promise to address a significant unmet clinical need to track NK cell fate in patients.

A reporter tomato line to track replication of a geminivirus in real time and with cellular resolution.

A reporter tomato line to track replication of a geminivirus in real time and with cellular resolution.

Testing Hubel and Wiesel's "ice-cube" model of functional maps at cellular resolution in macaque V1.

Hubel and Wiesel's ice-cube model proposed that V1 orientation and ocular dominance functional maps intersect orthogonally to optimize wiring efficiency. Here, we revisited this model and additional arrangements at both cellular and pixel levels in awake macaques using two-photon calcium imaging. The recorded response fields of view were similar in size to hypercolumns, each containing up to 2,000 identified neurons and representing full periods of orientation preferences and ocular dominance. We estimated each neuron/pixel's orientation, ocular dominance, and spatial frequency preferences, constructed respective functional maps, computed geometric gradients of feature preferences, and calculated intersection angles among these gradients. At the cellular level, the intersection angles among functional maps were nearly evenly distributed. Nonetheless, pixel-based maps after Gaussian smoothing displayed orientation-ocular dominance and orientation-spatial frequency orthogonality, as well as ocular dominance-spatial frequency parallelism, in alignment with previous results, even though the trends were weak and highly variable. However, these Gaussian smoothing effects were not observed in cellular maps, indicating that the pixel-based trends may not accurately represent the relationships among feature-tuning properties of V1 neurons. We suggest that the widely distributed intersections among cellular maps can ensure that multiple stimulus features are represented within a hypercolumn, and no pair of features is represented with the least economical wiring (e.g. parallel intersections).

An open-source software for the simulation of fixational eye movements

Abstract Laser-scanning confocal microscopy of the human cornea acquires in vivoimages of corneal tissues with cellular resolution, which offers diagnostic potential for a variety of diseases. However, involuntary fixational eye movements induce motion artefacts that pose a challenge for accurate morphometric analysis and particularly for preceding image data fusion steps. Different image registration algorithms promise to eliminate such artefacts, but objective, quantitative evaluation of the registration results is difficult because of the lack of ground truth data. Simulation approaches offer a possibility to close this gap. Existing open-source software for the simulation of fixational eye movements is currently limited to creating drift movement. The present contribution proposes complementary models for microsaccade and tremor components to provide complete simulated fixational eye movement paths. In addition, it reports results from an extensive examination of different model parameter combinations. It is shown that the microsaccade model is capable of reproducing intermicrosaccade intervals that closely resemble experimental data. A software implementation is provided as open-source Python modules.

Optomagnetic Micromirror Arrays for Mapping Large Area Stiffness Distributions of Biomimetic Materials.

A new device termed "Optomagnetic Micromirror Arrays" (OMA) is demonstrated capable of mapping the stiffness distribution of biomimetic materials across a 5.1 mm × 7.2 mm field of view with cellular resolution. The OMA device comprises an array of 50000 magnetic micromirrors with optical grating structures embedded beneath an elastic PDMS film, with biomimetic materials affixed on top. Illumination of a broadband white light beam onto these micromirrors results in the reflection of microscale rainbow light rays on each micromirror. When a magnetic field is applied, it causes each micromirror to tilt differently depending on the local stiffness of the biomimetic materials. Through imaging these micromirrors with low N.A. optics, a specific narrow band of reflection light rays from each micromirror is captured. Changing a micromirror's tilt angle also alters the color spectrum it reflects back to the imaging system and the color of the micromirror image it represents. As a result, OMA can infer the local stiffness of the biomimetic materials through the color change detected on each micromirror. OMA offers the potential for high-throughput stiffness mapping at the tissue-level while maintaining spatial resolution at the cellular level.

Adaptation in human immune cells residing in tissues at the frontline of infections

Human immune cells are under constant evolutionary pressure, primarily through their role as first line of defence against pathogens. Most studies on immune adaptation are, however, based on protein-coding genes without considering their cellular context. Here, using data from the Human Cell Atlas, we infer the gene adaptation rate of the human immune landscape at cellular resolution. We find abundant cell types, like progenitor cells during development and adult cells in barrier tissues, to harbour significantly increased adaptation rates. We confirm the adaptation of tissue-resident T and NK cells in the adult lung located in compartments directly facing external challenges, such as respiratory pathogens. Analysing human iPSC-derived macrophages responding to various challenges, we find adaptation in early immune responses. Together, our study suggests host benefits to control pathogen spread at early stages of infection, providing a retrospect of forces that shaped the complexity, architecture, and function of the human body.

Tailoring glioblastoma treatment based on longitudinal analysis of post-surgical tumor microenvironment

Glioblastoma (GBM), an incurable primary brain tumor, typically requires surgical intervention followed by chemoradiation; however, recurrences remain fatal. Our previous work demonstrated that a nanomedicine hydrogel (GemC12-LNC) delays recurrence when administered post-surgery. However, tumor debulking also triggers time-dependent immune reactions that promote recurrence at the resection cavity borders. We hypothesized that combining the hydrogel with an immunomodulatory drug could enhance therapeutic outcomes. A thorough characterization of the post-surgical microenvironment (SMe) is crucial to guide combinatorial approaches.In this study, we performed cellular resolution imaging, flow cytometry and spatial hyperplexed immunofluorescence imaging to characterize the SMe in a syngeneic mouse model of tumor resection. Owing to our dynamic approach, we observed transient opening of the blood–brain barrier (BBB) during the first week after surgery. BBB permeability post-surgery was also confirmed in GBM patients. In our murine model, we also observed changes in immune cell morphology and spatial location post-surgery over time in resected animals as well as the accumulation of reactive microglia and anti-inflammatory macrophages in recurrences compared to unresected tumors since the first steps of recurrence growth. Therefore we investigated whether starting a systemic treatment with the SMAC mimetic small molecule (GDC-0152) directly after surgery would be beneficial for enhancing microglial anti-tumoral activity and decreasing the number of anti-inflammatory macrophages around the GemC12-LNC hydrogel-loaded tumor cavity. The immunomodulatory effects of this drug combination was firstly shown in patient-derived tumoroids. Its efficacy was confirmed in vivo by survival analysis and correlated with reversal of the immune profile as well as delayed tumor recurrence.This comprehensive study identified critical time frames and immune cellular targets within the SMe, aiding in the rational design of combination therapies to delay recurrence onset. Our findings suggest that post-surgical systemic injection of GDC-0152 in combination with GemC12-LNC local treatment is a promising and innovative approach for managing GBM recurrence, with potential for future translation to human patient.Graphical

Single cell landscape of potential mechanisms in primary and metastatic hepatocellular carcinoma

Hepatocellular carcinoma metastasis occurs mainly in the portal vein and lymph node metastasis, and is the main cause of patient death. However, the cellular origins and driving mechanisms of hepatocellular carcinoma metastasis have not been elucidated. In this study, we found that different tumor metastasis samples originated from different branches of the primary tumor subclone. A large number of sequence data confirmed the correlation between tumor metastasis index and metastasis and prognosis. Patients with a high index generally had a poor prognosis and abnormal metabolic function. Finally, we recommended a candidate therapy for each metastatic direction. This study explains the cellular origin and underlying mechanisms of hepatocellular carcinoma metastasis at the single-cell level and identifies drugs for its targeted therapy. It also provides a new research idea for the study of hepatocellular carcinoma metastasis and early identification of cancer metastasis from cellular resolution.

In silico integrative scRNA analysis of human colonic epithelium indicates four tuft cell subtypes.

This study integrated and analyzed human single-cell RNA sequencing data from four publicly available datasets to enhance cellular resolution, unveiling a complex landscape of tuft cell heterogeneity within the human colon. Four tuft subtypes (TC1-TC4) emerged, as defined by unique gene expression profiles, indicating potentially novel biological functions. Tuft cell 1 (TC1) was characterized by an antimicrobial peptide signature; TC2 had an increased transcription machinery gene expression profile consistent with a progenitor-like cell; TC3 expressed genes related to ganglion (neuronal) development; and TC4 expressed genes related to tight junctions. Our analysis of subtype-specific gene expression and pathway enrichment showed variances in tuft cell subtypes between healthy individuals and those with inflammatory bowel disease (IBD). The frequency of TC1 and TC2 differed between healthy controls and IBD. Relative to healthy controls, TC1 and TC2 in IBD tissue showed an upregulation of gene expression, favoring increased metabolism and immune function. These findings provide foundational knowledge about the complexity of the human colon tuft cell population and hint at their potential contributions to gut health. They provide a basis for future studies to explore the specific roles these cells may play in gut function during homeostasis and disease. We demonstrate the value of in silico approaches for hypothesis generation in relation to the putative functions of low-frequency gut cells for subsequent physiological analyses.NEW & NOTEWORTHY This study reveals the nuanced and novel landscape of human colonic tuft cells through integrative scRNA-seq analysis. Four distinct tuft cell subtypes were identified, varying markedly between healthy and individuals with IBD. We uncovered human colonic tuft cell subtypes with unexpected antimicrobial and progenitor-like gene expression signatures. These insights into tuft cell diversity offer new avenues for understanding gut health and disease pathophysiology.

Classification of psychedelics and psychoactive drugs based on brain-wide imaging of cellular c-Fos expression.

Psilocybin, ketamine, and MDMA are psychoactive compounds that exert behavioral effects with distinguishable but also overlapping features. The growing interest in using these compounds as therapeutics necessitates preclinical assays that can accurately screen psychedelics and related analogs. We posit that a promising approach may be to measure drug action on markers of neural plasticity in native brain tissues. We therefore developed a pipeline for drug classification using light sheet fluorescence microscopy of immediate early gene expression at cellular resolution followed by machine learning. We tested male and female mice with a panel of drugs, including psilocybin, ketamine, 5-MeO-DMT, 6-fluoro-DET, MDMA, acute fluoxetine, chronic fluoxetine, and vehicle. In one-versus-rest classification, the exact drug was identified with 67% accuracy, significantly above the chance level of 12.5%. In one-versus-one classifications, psilocybin was discriminated from 5-MeO-DMT, ketamine, MDMA, or acute fluoxetine with >95% accuracy. We used Shapley additive explanation to pinpoint the brain regions driving the machine learning predictions. Our results support a novel approach for characterizing and validating psychoactive drugs with psychedelic properties.

Imaging the large-scale and cellular response to focal traumatic brain injury in mouse neocortex.

Traumatic brain injury (TBI) affects neural function at the local injury site and also at distant, connected brain areas. However, the real-time neural dynamics in response to injury and subsequent effects on sensory processing and behaviour are not fully resolved, especially across a range of spatial scales. We used in vivo calcium imaging in awake, head-restrained male and female mice to measure large-scale and cellular resolution neuronal activation, respectively, in response to a mild/moderate TBI induced by focal controlled cortical impact (CCI) injury of the motor cortex (M1). Widefield imaging revealed an immediate CCI-induced activation at the injury site, followed by a massive slow wave of calcium signal activation that travelled across the majority of the dorsal cortex within approximately 30s. Correspondingly, two-photon calcium imaging in the primary somatosensory cortex (S1) found strong activation of neuropil and neuronal populations during the CCI-induced travelling wave. A depression of calcium signals followed the wave, during which we observed the atypical activity of a sparse population of S1 neurons. Longitudinal imaging in the hours and days after CCI revealed increases in the area of whisker-evoked sensory maps at early time points, in parallel to decreases in cortical functional connectivity and behavioural measures. Neural and behavioural changes mostly recovered over hours to days in our M1-TBI model, with a more lasting decrease in the number of active S1 neurons. Our results in unanaesthetized mice describe novel spatial and temporal neural adaptations that occur at cortical sites remote to a focal brain injury.

Endogenous mitochondrial NAD(P)H fluorescence can predict lifespan

Many aging clocks have recently been developed to predict health outcomes and deconvolve heterogeneity in aging. However, existing clocks are limited by technical constraints, such as low spatial resolution, long processing time, sample destruction, and a bias towards specific aging phenotypes. Therefore, here we present a non-destructive, label-free and subcellular resolution approach for quantifying aging through optically resolving age-dependent changes to the biophysical properties of NAD(P)H in mitochondria through fluorescence lifetime imaging (FLIM) of endogenous NAD(P)H fluorescence. We uncover age-dependent changes to mitochondrial NAD(P)H across tissues in C. elegans that are associated with a decline in physiological function and construct non-destructive, label-free and cellular resolution models for prediction of age, which we refer to as “mito-NAD(P)H age clocks.” Mito-NAD(P)H age clocks can resolve heterogeneity in the rate of aging across individuals and predict remaining lifespan. Moreover, we spatiotemporally resolve age-dependent changes to mitochondria across and within tissues, revealing multiple modes of asynchrony in aging and show that longevity is associated with a ubiquitous attenuation of these changes. Our data present a high-resolution view of mitochondrial NAD(P)H across aging, providing insights that broaden our understanding of how mitochondria change during aging and approaches which expand the toolkit to quantify aging.

Volumetric imaging and computation to explore contractile function in zebrafish hearts.

Despite advancements in cardiovascular engineering, heart diseases remain a leading cause of mortality. The limited understanding of the underlying mechanisms of cardiac dysfunction at the cellular level restricts the development of effective screening and therapeutic methods. To address this, we have developed a framework that incorporates light field detection and individual cell tracking to capture real-time volumetric data in zebrafish hearts, which share structural and electrical similarities with the human heart and generate 120 to 180 beats per minute. Our results indicate that the in-house system achieves an acquisition speed of 200 volumes per second, with resolutions of up to 5.02 ± 0.54 µm laterally and 9.02 ± 1.11 µm axially across the entire depth, using the estimated-maximized-smoothed deconvolution method. The subsequent deep learning-based cell trackers enable further investigation of contractile dynamics, including cellular displacement and velocity, followed by volumetric tracking of specific cells of interest from end-systole to end-diastole in an interactive environment. Collectively, our strategy facilitates real-time volumetric imaging and assessment of contractile dynamics across the entire ventricle at the cellular resolution over multiple cycles, providing significant potential for exploring intercellular interactions in both health and disease.

Single nucleus RNA sequencing reveals cellular and molecular responses to vanadium exposure in duck kidneys

Vanadium (V) exposure is known to induce renal toxicity, yet its specific effects on renal cell types and molecular mechanisms remain incompletely understood. We used single nucleus RNA sequencing (snRNA-seq) to characterize the impact of V on duck kidney cells at a cellular resolution. Following a 44-day exposure, immunofluorescence analysis revealed a significant increase in α-SMC expression in the renal interstitium, indicative of fibrotic response. SnRNA-seq identified 12 major cell types organized into 19 clusters within the kidney. Significant changes in cell composition were observed, notably an increase in proximal tubule cells (PT2 subtype), glomerular endothelial cells, principal cells, and alterations in immune cell proportions, while collecting duct intercalated cells (CD-IC) and thick ascending limb showed decreased percentages. Differential gene expression analysis highlighted pathways implicated in V toxicity across different cell types. Changes in drug metabolism-cytochrome P450, butanoate metabolism, and actin cytoskeleton regulation were exhibited by PT cells. Alterations in collecting duct secretion, oxidative phosphorylation, and bicarbonate reclamation pathways were shown in CD-IC cells. Furthermore, immune cells displayed changes in T cell receptor and chemokine signaling pathways, indicative of altered immune responses. Taken together, these findings contribute to a better shedding light on the pathogenic mechanisms of V induced renal injury.