Electrical impedance assays of blood cells

In this review, the capability of electrical impedance spectroscopy analysis of blood cells, especially for red blood cells is presented, highlighting its large area of related biomedical relevance. The method is briefly introduced and basic theoretical aspects are discussed by considering both phenomenological (e.g. equivalent circuit) and microscopic approaches. The latter include a comparative analysis of the relevance of considering real shape (consistent with microscopic observations) versus spheroidal approximations (prolate and oblate spheroids) with the same surface and volume concentration. We show that while ellipsoidal approximation is fairly good for randomly oriented cells, it is quite poor whenever oriented cells are measured. The voluminous literature on the electrical analysis of blood cells is reviewed to stress the most promising biomedical applications of the method either per se or in combination with complementary e.g. (micro) fluidic approaches.     

Distributed medical image analysis and diagnosis through crowd-sourced games: a malaria case study

In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional.     

Metagenomic abundance estimation and diagnostic testing on species level

One goal of sequencing-based metagenomic community analysis is the quantitative taxonomic assessment of microbial community compositions. In particular, relative quantification of taxons is of high relevance for metagenomic diagnostics or microbial community comparison. However, the majority of existing approaches quantify at low resolution (e.g. at phylum level), rely on the existence of special genes (e.g. 16S), or have severe problems discerning species with highly similar genome sequences. Yet, problems as metagenomic diagnostics require accurate quantification on species level. We developed Genome Abundance Similarity Correction (GASiC), a method to estimate true genome abundances via read alignment by considering reference genome similarities in a non-negative LASSO approach. We demonstrate GASiC’s superior performance over existing methods on simulated benchmark data as well as on real data. In addition, we present applications to datasets of both bacterial DNA and viral RNA source. We further discuss our approach as an alternative to PCR-based DNA quantification.     

Quantitative assessment of peptide-lipid interactions.: Ubiquitous fluorescence methodologies

Peptide-membrane interactions have been gaining increased relevance, mainly in biomedical investigation, as the potential of the natural, nature-based and synthetic peptides as new drugs or drug candidates also expands. These peptides must face the cell membrane when they interfere with or participate in intracellular processes. Additionally, several peptide drugs and drug leads actions occur at the membrane level (e.g., antimicrobial peptides, cell-penetrating peptides and enveloped viruses membrane fusion inhibitors). Here we explore fluorescence spectroscopy methods that can be used to monitor such interactions. Two main approaches are considered, centered either on the peptide or on the membrane. On the first, we consider mainly the methodologies based on the intrinsic fluorescence of the aminoacid residues tryptophan and tyrosine. Regarding membrane-centric approaches, we review methods based on lipophilic probes sensitive to membrane potentials. The use of fluorescence constitutes a simple and sensitive method to measure these events. Unraveling the molecular mechanisms that govern these interactions can unlock the key to understand specific biological processes involving natural peptides or to optimize the action of a peptide drug.     

A Bayesian approach for subgroup analysis

Several penalization approaches have been developed to identify homogeneous subgroups based on a regression model with subject-specific intercepts in subgroup analysis. These methods often apply concave penalty functions to pairwise comparisons of the intercepts, such that the subjects with similar intercept values are assigned to the same group, which is very similar to the procedure of the penalization approaches for variable selection. Since the Bayesian methods are commonly used in variable selection, it is worth considering the corresponding approaches to subgroup analysis in the Bayesian framework. In this paper, a Bayesian hierarchical model with appropriate prior structures is developed for the pairwise differences of intercepts based on a regression model with subject-specific intercepts, which can automatically detect and identify homogeneous subgroups. A Gibbs sampling algorithm is also provided to select the hyperparameter and estimate the intercepts and coefficients of the covariates simultaneously, which is computationally efficient for pairwise comparisons compared to the time-consuming procedures for parameter estimation of the penalization methods (e.g., alternating direction method of multiplier) in the case of large sample sizes. The effectiveness and usefulness of the proposed Bayesian method are evaluated through simulation studies and analysis of a Cleveland Heart Disease Dataset.     

Evaluation of Ecotoxicity Effect Indicators for Use in LCIA (10+4 pp)

Goal, Scope and Background The paper describes different ecotoxicity effect indicator methods/approaches. The approaches cover three main groups, viz. PNEC approaches, PAF approaches and damage approaches. Ecotoxicity effect indicators used in life cycle impact assessment (LCIA) are typically modelled to the level of impact, indicating the potential impact on 'ecosystem health'. The few existing indicators, which are modelled all the way to damage, are poorly developed, and even though relevant alternatives from risk assessment exist (e.g. recovery time and mean extinction time), these are unfortunately at a very early stage of development, and only few attempts have been made to include them in LCIA. Methods The approaches are described and evaluated against a set of assessment criteria comprising compatibility with the methodological requirements of LCIA, environmental relevance, reproducibility, data demand, data availability, quantification of uncertainty, transparency and spatial differentiation. Results and Discussion The results of the evaluation of the two impact approaches (i.e. PNEC and PAF) show both pros and cons for each of them. The assessment factor-based PNEC approaches have a low data demand and use only the lowest data (e.g. lowest NOEC value). Because it is developed in tiered risk assessment, and hence makes use of conservative assessment factors, it is not optimal, in its present form, to use in the comparative framework of LCIA, where best estimates are sought. The PAF approaches have a higher data demand but use all data and can be based on effect data (PNEC is no-effect-based), thus making these approaches non-conservative and more suitable for LCIA. However, indiscriminate use of ecotoxicity data tends to make the PAF-approaches no more environmentally relevant than the assessment factor-based PNEC approaches. The PAF approaches, however, can at least in theory be linked to damage modelling. All the approaches for damage modelling which are included here have a high environmental relevance but very low data availability, apart from the 'media recovery-approach', which depends directly on the fate model. They are all at a very early stage of development. Conclusion Recommendations and Outlook. An analysis of the different PAF approaches shows that the crucial point is according to which principles and based on which data the hazardous concentration to 50% of the included species (i.e. HC50) is estimated. The ability to calculate many characterisation factors for ecotoxicity is important for this impact category to be included in LCIA in a proper way. However, the access to effect data for the relevant chemicals is typically limited. So, besides the coupling to damage modelling, the main challenge within the further development and improvement of ecotoxicity effect indicators is to find an optimal method to estimate HC50 based on little data.     

Label-free imaging and classification of live P. falciparum enables high performance parasitemia quantification without fixation or staining

Manual microscopic inspection of fixed and stained blood smears has remained the gold standard for Plasmodium parasitemia analysis for over a century. Unfortunately, smear preparation consumes time and reagents, while manual microscopy is skill-dependent and labor-intensive. Here, we demonstrate that deep learning enables both life stage classification and accurate parasitemia quantification of ordinary brightfield microscopy images of live, unstained red blood cells. We tested our method using both a standard light microscope equipped with visible and near-ultraviolet (UV) illumination, and a custom-built microscope employing deep-UV illumination. While using deep-UV light achieved an overall four-category classification of Plasmodium falciparum blood stages of greater than 99% and a recall of 89.8% for ring-stage parasites, imaging with near-UV light on a standard microscope resulted in 96.8% overall accuracy and over 90% recall for ring-stage parasites. Both imaging systems were tested extrinsically by parasitemia titration, revealing superior performance over manually-scored Giemsa-stained smears, and a limit of detection below 0.1%. Our results establish that label-free parasitemia analysis of live cells is possible in a biomedical laboratory setting without the need for complex optical instrumentation. We anticipate future extensions of this work could enable label-free clinical diagnostic measurements, one day eliminating the need for conventional blood smear analysis.     

Calibration of beam deflection produced by cellular forces in the 10(-9)--10(-6) gram range

An experimental procedure and method of analysis are presented for calibration of a thin-beam force transducer. The beam transducer can be produced and calibrated with a minimum coefficient of 10 ng (10(-5) dyne) force per micron (10(-4) cm) deflection, i.e., kB approximately 0.1 dyne/cm. Since beam deflections on the order of 0.1 micron can be detected, forces of a few nanograms can be resolved. Such forces are common in mechanical experiments on microscopic bodies, e.g., biological cells, artificial membrane capsules, droplets, etc.     

Use of combinatorial peptide libraries for T-cell epitope mapping

T lymphocytes play important roles not only in infectious diseases and autoimmunity, but also in immune responses against tumors. For many of these disorders, the relevant target antigens are not known. Designing effective methods that allow the search for T-cell epitopes is therefore an important goal in the areas of infectious diseases, oncology, vaccine development, and numerous other biomedical specialties. So far, the strategies used to examine T-cell recognition have been based largely on mapping T-cell epitopes with overlapping peptides from known proteins or with entire proteins, e.g., from a specific virus, bacterium, or human tissue. These approaches are tedious and have a number of limitations. It is, for example, almost impossible to isolate T cells that infiltrate an organ or infectious site and identify their specificity unless one already has a concept as to which antigens may be relevant. During recent years, a number of laboratories have developed less biased approaches that employ either the selection of putative T-cell epitopes based on the prediction of binding to certain major histocompatibilty complex (MHC) molecules and peptide or protein libraries that have been generated in expression systems, e.g. phage, or rely on combinatorial peptide chemistry. The latter technique has been refined by a number of laboratories including ours. Bead-bound or, preferably, positional scanning synthetic and soluble combinatorial peptide libraries allow the identification of T-cell epitopes within complex mixtures of proteins even for T cells that have been expanded from an organ infiltrate with a polyclonal stimulus. The practical steps that are involved in the latter method are described in this article.     

Absent MicroRNAs in Different Tissues of Patients with Acquired Cardiomyopathy

MicroRNAs (miRNAs) can be found in a wide range of tissues and body fluids, and their specific signatures can be used to determine diseases or predict clinical courses. The miRNA profiles in biological samples (tissue, serum, peripheral blood mononuclear cells or other body fluids) differ significantly even in the same patient and therefore have their own specificity for the presented con-dition. Complex profiles of deregulated miRNAs are of high interest, whereas the importance of non-expressed miRNAs was ignored. Since miRNAs regulate gene expression rather negatively, absent miRNAs could indicate genes with unaltered expression that therefore are normally expressed in specific compartments or under specific disease situations. For the first time, non-detectable miRNAs in different tissues and body fluids from patients with different diseases (cardiomyopathies, Alzheimer’s disease, bladder cancer, and ocular cancer) were analyzed and com-pared in this study. miRNA expression data were generated by microarray or TaqMan PCR-based platforms. Lists of absent miRNAs of primarily cardiac patients (myocardium, blood cells, and serum) were clustered and analyzed for potentially involved pathways using two prediction platforms, i.e., miRNA enrichment analysis and annotation tool (miEAA) and DIANA miRPath. Extensive search in biomedical publication databases for the relevance of non-expressed miRNAs in predicted pathways revealed no evidence for their involvement in heart-related pathways as indicated by software tools, confirming proposed approach.     

Using Cell-substrate Impedance and Live Cell Imaging to Measure Real-time Changes in Cellular Adhesion and De-adhesion Induced by Matrix Modification

Cell-matrix adhesion plays a key role in controlling cell morphology and signaling. Stimuli that disrupt cell-matrix adhesion (e.g., myeloperoxidase and other matrix-modifying oxidants/enzymes released during inflammation) are implicated in triggering pathological changes in cellular function, phenotype and viability in a number of diseases. Here, we describe how cell-substrate impedance and live cell imaging approaches can be readily employed to accurately quantify real-time changes in cell adhesion and de-adhesion induced by matrix modification (using endothelial cells and myeloperoxidase as a pathophysiological matrix-modifying stimulus) with high temporal resolution and in a non-invasive manner. The xCELLigence cell-substrate impedance system continuously quantifies the area of cell-matrix adhesion by measuring the electrical impedance at the cell-substrate interface in cells grown on gold microelectrode arrays. Image analysis of time-lapse differential interference contrast movies quantifies changes in the projected area of individual cells over time, representing changes in the area of cell-matrix contact. Both techniques accurately quantify rapid changes to cellular adhesion and de-adhesion processes. Cell-substrate impedance on microelectrode biosensor arrays provides a platform for robust, high-throughput measurements. Live cell imaging analyses provide additional detail regarding the nature and dynamics of the morphological changes quantified by cell-substrate impedance measurements. These complementary approaches provide valuable new insights into how myeloperoxidase-catalyzed oxidative modification of subcellular extracellular matrix components triggers rapid changes in cell adhesion, morphology and signaling in endothelial cells. These approaches are also applicable for studying cellular adhesion dynamics in response to other matrix-modifying stimuli and in related adherent cells (e.g., epithelial cells).     

Review on electromyography signal acquisition and processing

Electromyography (EMG) is a technique for recording biomedical electrical signals obtained from the neuromuscular activities. These signals are used to monitor medical abnormalities and activation levels, and also to analyze the biomechanics of any animal movements. In this article, we provide a short review of EMG signal acquisition and processing techniques. The average efficiency of capture of EMG signals with current technologies is around 70%. Once the signal is captured, signal processing algorithms then determine the recognition accuracy, with which signals are decoded for their corresponding purpose (e.g., moving robotic arm, speech recognition, gait analysis). The recognition accuracy can go as high as 99.8%. The accuracy with which the EMG signal is decoded has already crossed 99%, and with improvements in deep learning technology, there is a large scope for improvement in the design hardware that can efficiently capture EMG signals.     

Leveraging vibration of effects analysis for robust discovery in observational biomedical data science

Hypothesis generation in observational, biomedical data science often starts with computing an association or identifying the statistical relationship between a dependent and an independent variable. However, the outcome of this process depends fundamentally on modeling strategy, with differing strategies generating what can be called “vibration of effects” (VoE). VoE is defined by variation in associations that often lead to contradictory results. Here, we present a computational tool capable of modeling VoE in biomedical data by fitting millions of different models and comparing their output. We execute a VoE analysis on a series of widely reported associations (e.g., carrot intake associated with eyesight) with an extended additional focus on lifestyle exposures (e.g., physical activity) and components of the Framingham Risk Score for cardiovascular health (e.g., blood pressure). We leveraged our tool for potential confounder identification, investigating what adjusting variables are responsible for conflicting models. We propose modeling VoE as a critical step in navigating discovery in observational data, discerning robust associations, and cataloging adjusting variables that impact model output.

COVID positivity and vitamin D intake, red meat and heart disease; how can we discern when biomedical associations are reliable and when they are susceptible to our own arbitrary choices and assumptions? This study presents “quantvoe,” a software package for exploring the entirety of possible findings due to the multiverse of associations possible.     

Flat-floored Air-lifted Platform: A New Method for Combining Behavior with Microscopy or Electrophysiology on Awake Freely Moving Rodents

It is widely acknowledged that the use of general anesthetics can undermine the relevance of electrophysiological or microscopical data obtained from a living animal’s brain. Moreover, the lengthy recovery from anesthesia limits the frequency of repeated recording/imaging episodes in longitudinal studies. Hence, new methods that would allow stable recordings from non-anesthetized behaving mice are expected to advance the fields of cellular and cognitive neurosciences. Existing solutions range from mere physical restraint to more sophisticated approaches, such as linear and spherical treadmills used in combination with computer-generated virtual reality. Here, a novel method is described where a head-fixed mouse can move around an air-lifted mobile homecage and explore its environment under stress-free conditions. This method allows researchers to perform behavioral tests (e.g., learning, habituation or novel object recognition) simultaneously with two-photon microscopic imaging and/or patch-clamp recordings, all combined in a single experiment. This video-article describes the use of the awake animal head fixation device (mobile homecage), demonstrates the procedures of animal habituation, and exemplifies a number of possible applications of the method.     

A method for the rapid exchange of solutions bathing excised membrane patches.

In this communication we describe a technique for rapidly exchanging solutions bathing excised membrane patches, and present examples of its implementation using both outside-out and inside-out patches. The ability to make step changes in the concentration of channel-activating ligands (e.g., acetylcholine, calcium) offers a novel and direct means of measuring kinetic processes in the 10-100-ms range. The responses to step ligand concentration changes are well suited to ensemble variance analysis, yielding estimates of the number of channels in a patch, and testing assumptions of channel independence and homogeneity. Kinetic analysis of the pseudomacroscopic currents obtained by averaging large numbers of responses can be compared and correlated with analysis of the microscopic behavior of single channels, using the same membrane patch for both approaches. Practical and theoretical limitations associated with the method are briefly discussed.     

A multimodal system for reconstruction and quantification of neurologic structures

The utility of computers and computer graphics as aids in the study of nervous system architecture is growing. However, modern histologic, immunocytologic and biochemical methods for revealing the underlying microarchitecture and macroarchitecture of the nervous system yield data formats requiring disparate computer acquisition, analysis and display approaches, capable of spanning many orders of magnitude of scale. This paper describes the Image Graphics Laboratory data acquisition, processing and display system, whose various components and programs may be used singly or in concert to enable definition of various tissue properties at different levels of resolution and integration. Examples are given of the system's use in light microscopic two-dimensional and three-dimensional reconstructions, autoradiographic reconstructions, reconstructions from projected images, reconstructions of impregnated cells (e.g., whole neurons) and peripheral nerve image analysis.     

Determination of intracellular conductivity from electrical breakdown measurements

The intracellular resistivity (conductivity) of cells can be easily calculated with high accuracy from electrical membrane breakdown measurements. The method is based on the determination of the size distribution of a cell suspension as a function of the electrical field strength in the orifice of a particle volume analyser (Coulter counter). The underestimation of the size distribution observed beyond the critical external field strength leading to membrane breakdown represents a direct access to the intracellular resistivity as shown by the theoretical analysis of the data. The potential and the accuracy of the method is demonstrated for red blood cells and for ghost cells prepared by electrical haemolysis. The average value of 180 omega X cm for the intracellular resistivity of intact red blood cells is consistent with the literature.     

Quantification and cluster analysis of actin cytoskeletal structures in plant cells: role of actin bundling in stomatal movement during diurnal cycles in Arabidopsis guard cells

Manual evaluation of cellular structures is a popular approach in cell biological studies. However, such approaches are laborious and are prone to error, especially when large quantities of image data need to be analyzed. Here, we introduce an image analysis framework that overcomes these limitations by semi-automatic quantification and clustering of cytoskeletal structures. In our framework, cytoskeletal orientation, bundling and density are quantified by measurement of newly-developed, robust metric parameters from microscopic images. Thereafter, the microscopic images are classified without supervision by clustering based on the metric patterns. Clustering allows us to collectively investigate the large number of cytoskeletal structure images without laborious inspection. Application of this framework to images of GFP-actin binding domain 2 (GFP-ABD2)-labeled actin cytoskeletons in Arabidopsis guard cells determined that microfilaments (MFs) are radially oriented and transiently bundled in the process of diurnal stomatal opening. The framework also revealed that the expression of mouse talin GFP-ABD (GFP-mTn) continuously induced MF bundling and suppressed the diurnal patterns of stomatal opening, suggesting that changes in the level of MF bundling are crucial for promoting stomatal opening. These results clearly demonstrate the utility of our image analysis framework.     

DNA methylation arrays as surrogate measures of cell mixture distribution

ABSTRACT: BACKGROUND: There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls. RESULTS: Here we present a method, similar to regression calibration, for inferring changes in the distribution of white blood cells between different subpopulations (e.g. cases and controls) using DNA methylation signatures, in combination with a previously obtained external validation set consisting of signatures from purified leukocyte samples. We validate the fundamental idea in a cell mixture reconstruction experiment, then demonstrate our method on DNA methylation data sets from several studies, including data from a Head and Neck Squamous Cell Carcinoma (HNSCC) study and an ovarian cancer study. Our method produces results consistent with prior biological findings, thereby validating the approach. CONCLUSIONS: Our method, in combination with an appropriate external validation set, promises new opportunities for large-scale immunological studies of both disease states and noxious exposures.