Leveraging cell-cell similarity for high-performance spatial and temporal cellular mappings from gene expression data
Ontology highlight
ABSTRACT:
SUBMITTER: Islam M
PROVIDER: S-EPMC10591141 | biostudies-literature | 2023 Sep
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Traffic congestion varies spatially and temporally. The observation of the formation, propagation and dispersion of network traffic congestion can lead to insights about the network performance, the bottleneck dynamics etc. While many researchers use the traffic flow data to reconstruct the congestion profile, the data missing problem is bypassed. Current methods either omit the missing data or supplement the missing part by average etc. Great error may be introduced during these processes. Rather than simply discarding the missing data, this research regards the data missing event as a result of either the severe congestion which prevent the floating vehicle from entering the congested area, or a type of feature of the resulting traffic flow time series. Hence a new traffic flow operational index time series similarity measurement is expected to be established as a basis of identifying the dynamic network bottleneck. The method first measures the traffic flow operational similarity between pairs of neighboring links, and then the similarity results are used to cluster the spatial-temporal congestion. In order to get the similarity under missing data condition, the measurement is implemented in a two-stage manner: firstly the so called first order similarity is calculated given that the traffic flow variables are bounded both upside and downside; then the first order similarity is aggregated to generate the second order similarity as the output. We implement the method on part of the real-world road network; the results generated are not only consistent with empirical observation, but also provide useful insights.
| S-EPMC5029889 | biostudies-literature
Project description:Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in health and disease. However, the lack of physical relationships among dissociated cells has limited its applications. To address this issue, we present CeLEry (Cell Location recovEry), a supervised deep learning algorithm that leverages gene expression and spatial location relationships learned from spatial transcriptomics to recover the spatial origins of cells in scRNA-seq. CeLEry has an optional data augmentation procedure via a variational autoencoder, which improves the method's robustness and allows it to overcome noise in scRNA-seq data. We show that CeLEry can infer the spatial origins of cells in scRNA-seq at multiple levels, including 2D location and spatial domain of a cell, while also providing uncertainty estimates for the recovered locations. Our comprehensive benchmarking evaluations on multiple datasets generated from brain and cancer tissues using Visium, MERSCOPE, MERFISH, and Xenium demonstrate that CeLEry can reliably recover the spatial location information for cells using scRNA-seq data.
| S-EPMC10329686 | biostudies-literature
Project description:One of the major challenges in developing acoustic droplet vaporization (ADV)-associated therapy as an effective and safe strategy is the precise determination of the spatial cellular bioeffects after ADV (cell death or cell membrane permeabilization). In this study, we combined high-speed camera imaging and live-cell microscopic imaging to observe the transient dynamics of droplets during ADV and to evaluate the mechanical force on cells. Methods: C6 glioma cells were co-incubated with DiI-labeled droplets (radius: 1.5, 2.25, and 3.0 μm). We used an acousto-optical system for high-speed bright-field (500 kfps) and fluorescence (40 kfps) microscopic imaging in order to visualize the dynamics of droplets under ultrasound excitation (frequency = 5 MHz, pressure = 5-8 MPa, cycle number = 3, pulse number = 1). Live-cell microscopic imaging was used to monitor the cell morphology, cell membrane permeabilization, and cell viability by membrane-anchored Lyn-yellow fluorescence protein, propidium Iodide staining, and calcein blue AM staining, respectively. Results: We discovered that the spatial distribution of ADV-induced bioeffects could be mapped to the physical dynamics of droplet vaporization. For droplets with a 1.5 μm radius, the distance threshold for ADV-induced cell death (5.5±1.9 μm) and reversible membrane permeabilization (11.3±3.5 μm) was well correlated with the distance of ADV-bubble pressing downward to the floor (5.7±1.3 μm) and maximum distance of droplet expansion (11.5±2.6 μm), respectively. These distances were enlarged by increasing the droplet sizes and insonation acoustic pressures. The live-cell imaging results show that ADV-bubbles can directly disrupt the cell membrane layer and induce intensive intracellular substance leakage. Further, the droplets shed the payload onto nearby cells during ADV, suggesting ADV could directly induce adjacent cell death by physical force and enhancement of chemotherapy to distant cells. Conclusion: This study provide new insights into the ADV-mediated physicochemical synergic effect for medical applications.
| S-EPMC6276289 | biostudies-literature
Project description:Similarity measures based on the comparison of dense bit vectors of two-dimensional chemical features are a dominant method in chemical informatics. For large-scale problems, including compound selection and machine learning, computing the intersection between two dense bit vectors is the overwhelming bottleneck. We describe efficient implementations of this primitive as well as example applications using features of modern CPUs that allow 20-40× performance increases relative to typical code. Specifically, we describe fast methods for population count on modern x86 processors and cache-efficient matrix traversal and leader clustering algorithms that alleviate memory bandwidth bottlenecks in similarity matrix construction and clustering. The speed of our 2D comparison primitives is within a small factor of that obtained on GPUs and does not require specialized hardware.
| S-EPMC4839782 | biostudies-literature
Project description:Oxygen is a key modulator of many cellular pathways, but current devices permitting in vitro oxygen modulation fail to meet the needs of biomedical research. A microfabricated insert for multiwell plates has been developed to more effectively control the temporal and spatial oxygen concentration to better model physiological phenomena found in vivo. The platform consists of a polydimethylsiloxane insert that nests into a standard multiwell plate and serves as a passive microfluidic gas network with a gas-permeable membrane aimed to modulate oxygen delivery to adherent cells. Equilibration time is on the order of minutes and a wide variety of oxygen profiles can be attained based on the device design, such as the cyclic profile achieved in this study, and even oxygen gradients to mimic those found in vivo. The proper biological consequences of the device's oxygen delivery were confirmed in cellular models via a proliferation assay and western analysis of the upregulation of hypoxia inducible transcription factor-1alpha. These experiments serve as a demonstration for the platform as a viable tool to increase experimental throughput and permit novel experimental possibilities in any biomedical research lab.
| S-EPMC2731542 | biostudies-literature
Project description:We develop a quantitative single cell-based mathematical model for multi-cellular tumor spheroids (MCTS) of SK-MES-1 cells, a non-small cell lung cancer (NSCLC) cell line, growing under various nutrient conditions: we confront the simulations performed with this model with data on the growth kinetics and spatial labeling patterns for cell proliferation, extracellular matrix (ECM), cell distribution and cell death. We start with a simple model capturing part of the experimental observations. We then show, by performing a sensitivity analysis at each development stage of the model that its complexity needs to be stepwise increased to account for further experimental growth conditions. We thus ultimately arrive at a model that mimics the MCTS growth under multiple conditions to a great extent. Interestingly, the final model, is a minimal model capable of explaining all data simultaneously in the sense, that the number of mechanisms it contains is sufficient to explain the data and missing out any of its mechanisms did not permit fit between all data and the model within physiological parameter ranges. Nevertheless, compared to earlier models it is quite complex i.e., it includes a wide range of mechanisms discussed in biological literature. In this model, the cells lacking oxygen switch from aerobe to anaerobe glycolysis and produce lactate. Too high concentrations of lactate or too low concentrations of ATP promote cell death. Only if the extracellular matrix density overcomes a certain threshold, cells are able to enter the cell cycle. Dying cells produce a diffusive growth inhibitor. Missing out the spatial information would not permit to infer the mechanisms at work. Our findings suggest that this iterative data integration together with intermediate model sensitivity analysis at each model development stage, provide a promising strategy to infer predictive yet minimal (in the above sense) quantitative models of tumor growth, as prospectively of other tissue organization processes. Importantly, calibrating the model with two nutriment-rich growth conditions, the outcome for two nutriment-poor growth conditions could be predicted. As the final model is however quite complex, incorporating many mechanisms, space, time, and stochastic processes, parameter identification is a challenge. This calls for more efficient strategies of imaging and image analysis, as well as of parameter identification in stochastic agent-based simulations.
| S-EPMC4750943 | biostudies-literature
Project description:Large-scale environmental sequencing efforts have transformed our understanding of the spatial controls over soil microbial community composition and turnover. Yet, our knowledge of temporal controls is comparatively limited. This is a major uncertainty in microbial ecology, as there is increasing evidence that microbial community composition is important for predicting microbial community function in the future. Here, we use continental- and global-scale soil fungal community surveys, focused within northern temperate latitudes, to estimate the relative contribution of time and space to soil fungal community turnover. We detected large intra-annual temporal differences in soil fungal community similarity, where fungal communities differed most among seasons, equivalent to the community turnover observed over thousands of kilometers in space. inter-annual community turnover was comparatively smaller than intra-annual turnover. Certain environmental covariates, particularly climate covariates, explained some spatial-temporal effects, though it is unlikely the same mechanisms drive spatial vs. temporal turnover. However, these commonly measured environmental covariates could not fully explain relationships between space, time and community composition. These baseline estimates of fungal community turnover in time provide a starting point to estimate the potential duration of legacies in microbial community composition and function.
| S-EPMC6776031 | biostudies-literature
Project description:Next generation sequencing (NGS) has been a great success and is now a standard method of research in the life sciences. With this technology, dozens of whole genomes or hundreds of exomes can be sequenced in rather short time, producing huge amounts of data. Complex bioinformatics analyses are required to turn these data into scientific findings. In order to run these analyses fast, automated workflows implemented on high performance computers are state of the art. While providing sufficient compute power and storage to meet the NGS data challenge, high performance computing (HPC) systems require special care when utilized for high throughput processing. This is especially true if the HPC system is shared by different users. Here, stability, robustness and maintainability are as important for automated workflows as speed and throughput. To achieve all of these aims, dedicated solutions have to be developed. In this paper, we present the tricks and twists that we utilized in the implementation of our exome data processing workflow. It may serve as a guideline for other high throughput data analysis projects using a similar infrastructure. The code implementing our solutions is provided in the supporting information files.
| S-EPMC4420499 | biostudies-literature
Project description:BACKGROUND:We previously demonstrated that morphologic defects of ileal Paneth cells correlate with multiple susceptible genetic variants, the presence of granuloma, and clinical outcome in Crohn's disease. These studies were performed using uninvolved areas of resection specimens. To develop Paneth cell phenotype as a prognostic biomarker in Crohn's disease, further characterization is necessary. Specifically, effects of disease activity, phenotype duration, and the minimal crypt number that would allow for accurate Paneth cell phenotyping are unknown. METHODS:We compared Paneth cell phenotypes in (1) 46 cases with paired involved and uninvolved sections; (2) 36 cases with multiple ileal resections over time; (3) "virtual biopsies" by randomly selecting 10 to 60 crypts from 85 surgical cases where 250 crypts had been analyzed; and (4) 26 cases with resection and biopsy performed within 1 year. RESULTS:In paired resection specimens, the Paneth cell phenotypes in the uninvolved areas correlated with those seen in involved areas (P < 0.0001) and also predicted the presence of granuloma (P = 0.042). Importantly, the Paneth cell phenotype remained stable over time (P < 0.0001). By mathematical analyses, a minimum of 40 crypts was required to generate results equivalent to those using resection specimens. Finally, there was good correlation in Paneth cell phenotypes in biopsy specimens and resection specimens obtained within 1 year (P = 0.0004). CONCLUSIONS:Accurate Paneth cell phenotypes can be assessed using biopsy materials with the caveat that sufficient well-oriented crypts exist in the specimen. This advance will extend the potential clinical application of this novel stratification platform.
| S-EPMC4180521 | biostudies-literature
Project description:Despite its critical function in coordinating the egress of inflammatory and immune cells out of tissues and maintaining fluid balance, the causative role of lymphatic network dysfunction in pathological settings is still understudied. Engineered-animal models and better noninvasive high spatial-temporal resolution imaging techniques in both preclinical and clinical studies will help to improve our understanding of different lymphatic-related pathologic disorders. Our aim was to take advantage of our newly optimized noninvasive wide-field fast-scanning photoacoustic (PA) microcopy system to coordinately image the lymphatic vasculature and its flow dynamics, while maintaining high resolution and detection sensitivity. Here, by combining the optical-resolution PA microscopy with a fast-scanning water-immersible microelectromechanical system scanning mirror, we have imaged the lymph dynamics over a large field-of-view, with high spatial resolution and advanced detection sensitivity. Depending on the application, lymphatic vessels (LV) were spectrally or temporally differentiated from blood vessels. Validation experiments were performed on phantoms and in vivo to identify the LV. Lymphatic flow dynamics in nonpathological and pathological conditions were also visualized. These results indicate that our newly developed PA microscopy is a promising tool for lymphatic-related biological research.
| S-EPMC4407768 | biostudies-literature