Numerical optimization of gene electrotransfer into muscle tissue

<p>Abstract</p> <p>Background</p> <p>Electroporation-based gene therapy and DNA vaccination are promising medical applications that depend on transfer of pDNA into target tissues with use of electric pulses. Gene electrotransfer efficiency depends on electrode configuration and electric pulse parameters, which determine the electric field distribution. Numerical modeling represents a fast and convenient method for optimization of gene electrotransfer parameters. We used numerical modeling, parameterization and numerical optimization to determine the optimum parameters for gene electrotransfer in muscle tissue.</p> <p>Methods</p> <p>We built a 3D geometry of muscle tissue with two or six needle electrodes (two rows of three needle electrodes) inserted. We performed a parametric study and optimization based on a genetic algorithm to analyze the effects of distances between the electrodes, depth of insertion, orientation of electrodes with respect to muscle fibers and applied voltage on the electric field distribution. The quality of solutions were evaluated in terms of volumes of reversibly (desired) and irreversibly (undesired) electroporated muscle tissue and total electric current through the tissue.</p> <p>Results</p> <p>Large volumes of reversibly electroporated muscle with relatively little damage can be achieved by using large distances between electrodes and large electrode insertion depths. Orienting the electrodes perpendicular to muscle fibers is significantly better than the parallel orientation for six needle electrodes, while for two electrodes the effect of orientation is not so pronounced. For each set of geometrical parameters, the window of optimal voltages is quite narrow, with lower voltages resulting in low volumes of reversibly electroporated tissue and higher voltages in high volumes of irreversibly electroporated tissue. Furthermore, we determined which applied voltages are needed to achieve the optimal field distribution for different distances between electrodes.</p> <p>Conclusion</p> <p>The presented numerical study of gene electrotransfer is the first that demonstrates optimization of parameters for gene electrotransfer on tissue level. Our method of modeling and optimization is generic and can be applied to different electrode configurations, pulsing protocols and different tissues. Such numerical models, together with knowledge of tissue properties can provide useful guidelines for researchers and physicians in selecting optimal parameters for <it>in vivo </it>gene electrotransfer, thus reducing the number of animals used in studies of gene therapy and DNA vaccination.</p

Automatic cell counter for cell viability estimation

Despite several methods that exist in different fields of life sciences, certain biotechnological applications still require microscopic analysis of the samples and in many instances, counting of cells. Some of those are drug delivery, transfection or analysis of mechanism fluorescent probes are used to detect cell viability, efficiency of a specific drug delivery or some other effect. For analysis and quantification of these results it is necessary to either manually or automatically count and analyze microscope images. However, in everyday use many researchers still count cells manually since existing solutions require either some specific knowledge of computer vision and/or manual fine tuning of various parameters. Here we present a new software solution (named CellCounter) for automatic and semi-automatic cell counting of fluorescent microscopic images. This application is specifically designed for counting fluorescently stained cells. The program enables counting of cell nuclei or cell cytoplasm stained with different fluorescent stained. This simplifies image analysis for several biotechnological applications where fluorescent microscopy is used. We present results and validate the presented automatic cell counting program for cell viability application. We give empirical results showing the efficiency of the proposed solution by comparing manual counts with the results returned by automated counting. We also show how the results can be further improved by combining manual and automated

Automatic cell counter for cell viability estimation

Despite several methods that exist in different fields of life sciences, certain biotechnological applications still require microscopic analysis of the samples and in many instances, counting of cells. Some of those are drug delivery, transfection or analysis of mechanism fluorescent probes are used to detect cell viability, efficiency of a specific drug delivery or some other effect. For analysis and quantification of these results it is necessary to either manually or automatically count and analyze microscope images. However, in everyday use many researchers still count cells manually since existing solutions require either some specific knowledge of computer vision and/or manual fine tuning of various parameters. Here we present a new software solution (named CellCounter) for automatic and semi-automatic cell counting of fluorescent microscopic images. This application is specifically designed for counting fluorescently stained cells. The program enables counting of cell nuclei or cell cytoplasm stained with different fluorescent stained. This simplifies image analysis for several biotechnological applications where fluorescent microscopy is used. We present results and validate the presented automatic cell counting program for cell viability application. We give empirical results showing the efficiency of the proposed solution by comparing manual counts with the results returned by automated counting. We also show how the results can be further improved by combining manual and automated

Automatic adaptation of filter sequences for cell counting

Manual cell counting in microscopic images is usually tedious, time consuming and prone to human error. Several programs for automatic cell counting have been developed so far, but most of them demand some specific knowledge of image analysis and/or manual fine tuning of various parameters. Even if a set of filters is found and fine tuned to the specific application, small changes to the image attributes might make the automatic counter very unreliable. The goal of this article is to present a new application that overcomes this problem by learning the set of parameters for each application, thus making it more robust to changes in the input images. The users must provide only a small representative subset of images and their manual count, and the program offers a set of automatic counters learned from the given input. The user can check the counters and choose the most suitable one. The resulting application (which we call Learn123) is specifically tailored to the practitioners, i.e. even though the typical workflow is more complex, the application is easy to use for non-technical experts

Automatic adaptation of filter sequences for cell counting

Manual cell counting in microscopic images is usually tedious, time consuming and prone to human error. Several programs for automatic cell counting have been developed so far, but most of them demand some specific knowledge of image analysis and/or manual fine tuning of various parameters. Even if a set of filters is found and fine tuned to the specific application, small changes to the image attributes might make the automatic counter very unreliable. The goal of this article is to present a new application that overcomes this problem by learning the set of parameters for each application, thus making it more robust to changes in the input images. The users must provide only a small representative subset of images and their manual count, and the program offers a set of automatic counters learned from the given input. The user can check the counters and choose the most suitable one. The resulting application (which we call Learn123) is specifically tailored to the practitioners, i.e. even though the typical workflow is more complex, the application is easy to use for non-technical experts

Comparison of two automatic cell-counting solutions for fluorescent microscopic images

Cell counting in microscopic images is one of the fundamental analysis tools in life sciences, but is usually tedious, time consuming and prone to human error. Several programs for automatic cell countinghavebeendeveloped sofar,butmostof them demand additional training or data input from the user. Most of them do not allow the users to online monitor the counting results, either. Therefore, we designed two straightforward, simple-to-use cell-counting programs that also allow users to correct the detection results. In this paper, we present the CELLCOUNTER and LEARN123 programs for automatic and semiautomatic counting of objects in fluorescent microscopic images (cells or cell nuclei) with a user-friendly interface. Although CELLCOUNTER is based on predefined and fine-tuned set of filters optimized on sets of chosen experiments, LEARN123 uses an evolutionary algorithm to determine the adapt filter parameters based on a learning set of images. CELLCOUNTER also includes an extension for analysis of overlaying images. The efficiency of both programs was assessed on images of cells stained with different fluorescent dyes by comparing automatically obtained results with results that were manually annotated by an expert. With both programs, the correlation between automatic and manual counting was very high (R2 < 0.9), although CELLCOUNTER had some difficulties processing images with no cells or weakly stained cells, where sometimes the background noise was recognized as an object of interest. Nevertheless, the differences between manual and automatic counting were small compared to variations between experimental repeats. Both programs significantly reduced the time required to process the acquired images from hours tominutes. The programs enable consistent, robust, fast and accurate detection of fluorescent objects and can therefore be applied to a range of different applications in different fields of life sciences where fluorescent labelling is used for quantification of various phenomena. Moreover, CELLCOUNTER overlay extension also enables fast analysis of related images thatwouldotherwise require imagemergingforaccurateanalysis, whereas LEARN123’s evolutionary algorithm can adapt counting parameters to specific sets of images of different experimental settings

Alterations in immunophenotype and metabolic profile of mononuclear cells during follow up in children with multisystem inflammatory syndrome (MIS-C)

IntroductionAlthough children seem to be less susceptible to COVID-19, some of them develop a rare but serious hyperinflammatory condition called multisystem inflammatory syndrome in children (MIS-C). While several studies describe the clinical conditions of acute MIS-C, the status of convalescent patients in the months after acute MIS-C is still unclear, especially the question of persistence of changes in the specific subpopulations of immune cells in the convalescent phase of the disease.MethodsWe therefore analyzed peripheral blood of 14 children with MIS-C at the onset of the disease (acute phase) and 2 to 6 months after disease onset (post-acute convalescent phase) for lymphocyte subsets and antigen-presenting cell (APC) phenotype. The results were compared with six healthy age-matched controls.ResultsAll major lymphocyte populations (B cells, CD4 + and CD8+ T cells, and NK cells) were decreased in the acute phase and normalized in the convalescent phase. T cell activation was increased in the acute phase, followed by an increased proportion of γ/δ-double-negative T cells (γ/δ DN Ts) in the convalescent phase. B cell differentiation was impaired in the acute phase with a decreased proportion of CD21 expressing, activated/memory, and class-switched memory B cells, which normalized in the convalescent phase. The proportion of plasmacytoid dendritic cells, conventional type 2 dendritic cells, and classical monocytes were decreased, while the proportion of conventional type 1 dendritic cells was increased in the acute phase. Importantly the population of plasmacytoid dendritic cells remained decreased in the convalescent phase, while other APC populations normalized. Immunometabolic analysis of peripheral blood mononuclear cells (PBMCs) in the convalescent MIS-C showed comparable mitochondrial respiration and glycolysis rates to healthy controls.ConclusionsWhile both immunophenotyping and immunometabolic analyzes showed that immune cells in the convalescent MIS-C phase normalized in many parameters, we found lower percentage of plasmablasts, lower expression of T cell co-receptors (CD3, CD4, and CD8), an increased percentage of γ/δ DN Ts and increased metabolic activity of CD3/CD28-stimulated T cells. Overall, the results suggest that inflammation persists for months after the onset of MIS-C, with significant alterations in some immune system parameters, which may also impair immune defense against viral infections