Automated DNA Fragments Recognition and Sizing through AFM Image Processing

This paper presents an automated algorithm to determine DNA fragment size from atomic force microscope images and to extract the molecular profiles. The sizing of DNA fragments is a widely used procedure for investigating the physical properties of individual or protein-bound DNA molecules. Several atomic force microscope (AFM) real and computer-generated images were tested for different pixel and fragment sizes and for different background noises. The automated approach minimizes processing time with respect to manual and semi-automated DNA sizing. Moreover, the DNA molecule profile recognition can be used to perform further structural analysis. For computer-generated images, the root mean square error incurred by the automated algorithm in the length estimation is 0.6% for a 7.8 nm image pixel size and 0.34% for a 3.9 nm image pixel size. For AFM real images we obtain a distribution of lengths with a standard deviation of 2.3% of mean and a measured average length very close to the real one, with an error around 0.33%

Label-free, atomic force microscopy-based mapping of DNA intrinsic curvature for the nanoscale comparative analysis of bent duplexes

We propose a method for the characterization of the local intrinsic curvature of adsorbed DNA molecules. It relies on a novel statistical chain descriptor, namely the ensemble averaged product of curvatures for two nanosized segments, symmetrically placed on the contour of atomic force microscopy imaged chains. We demonstrate by theoretical arguments and experimental investigation of representative samples that the fine mapping of the average product along the molecular backbone generates a characteristic pattern of variation that effectively highlights all pairs of DNA tracts with large intrinsic curvature. The centrosymmetric character of the chain descriptor enables targetting strands with unknown orientation. This overcomes a remarkable limitation of the current experimental strategies that estimate curvature maps solely from the trajectories of end-labeled molecules or palindromes. As a consequence our approach paves the way for a reliable, unbiased, label-free comparative analysis of bent duplexes, aimed to detect local conformational changes of physical or biological relevance in large sample numbers. Notably, such an assay is virtually inaccessible to the automated intrinsic curvature computation algorithms proposed so far. We foresee several challenging applications, including the validation of DNA adsorption and bending models by experiments and the discrimination of specimens for genetic screening purposes

Image Processing of SEM Image Nano Silver Using K-means MATLAB Technique

Nanotechnology is one of the non-exhaustive applications in which image processing is used. For optimal nanoparticle visualization and characterization, the high resolution Scanning Electron Microscope (SEM) and the Atomic Force Microscope (AFM) are used. Image segmentation is one of the critical steps in nanoscale processing. There are also different ways to reach retail, including statistical approximations.In this study; we used the K-means method to determine the optimal threshold using statistical approximation. This technique is thoroughly studied for the SEM nanostructure Silver image. Note that, the image obtained by SEM is good enough to analyze more recently images. The analysis is being used in the field of nanotechnology. The K-means algorithm classifies the data set given to k groups based on certain measurements of certain distances. K-means technology is the most widely used among all clustering algorithms. It is one of the common techniques used in statistical data analysis, image analysis, neural networks, classification analysis and biometric information. K-means is one of the fastest collection algorithms and can be easily used in image segmentation. The results showed that K-means is highly sensitive to small data sets and performance can degrade at any time. When exposed to a huge data set such as 100.000, the performance increases significantly. The algorithm also works well when the number of clusters is small. This technology has helped to provide a good performance algorithm for the state of the image being tested

Joint co-clustering: co-clustering of genomic and clinical bioimaging data

AbstractFor better understanding the genetic mechanisms underlying clinical observations, and better defining a group of potential candidates for protein-family-inhibiting therapy, it is interesting to determine the correlations between genomic, clinical data and data coming from high resolution and fluorescent microscopy. We introduce a computational method, called joint co-clustering, that can find co-clusters or groups of genes, bioimaging parameters and clinical traits that are believed to be closely related to each other based on the given empirical information. As bioimaging parameters, we quantify the expression of growth factor receptor EGFR/erb-B family in non-small cell lung carcinoma (NSCLC) through a fully-automated computer-aided analysis approach. This immunohistochemical analysis is usually performed by pathologists via visual inspection of tissue samples images. Our fully-automated techniques streamlines this error-prone and time-consuming process, thereby facilitating analysis and diagnosis. Experimental results for several real-life datasets demonstrate the high quantitative precision of our approach. The joint co-clustering method was tested with the receptor EGFR/erb-B family data on non-small cell lung carcinoma (NSCLC) tissue and identified statistically significant co-clusters of genes, receptor protein expression and clinical traits. The validation of our results with the literature suggest that the proposed method can provide biologically meaningful co-clusters of genes and traits and that it is a very promising approach to analyse large-scale biological data and to study multi-factorial genetic pathologies through their genetic alterations

DNA nanomapping using CRISPR-Cas9 as a programmable nanoparticle

Physical mapping of DNA can be used to detect structural variants and for whole-genome haplotype assembly. Here, the authors use CRISPR-Cas9 and high-speed atomic force microscopy to ‘nanomap’ single molecules of DNA

Analysis of intrinsic DNA curvature in the TP53 tumour suppressor gene using atomic force microscopy.

The research described in this thesis aimed to evaluate the intrinsic DNA curvature ofthe region of the TP53 tumour suppressor gene that codes for the sequence-specific DNA-binding domain of the p53 protein, a key protein that protects the cell from chemical insultsand tumourogenesis. There have been no previous attempts to experimentally investigate theintrinsic DNA curvature within TP53 or its relation to the functional or structural properties ofthe gene, such as DNA repair and nucleosomal architecture. The present study usedtheoretical models of TP53 in concert with an atomic force microscopy based experimentalinvestigation of TP53 DNA molecules to analyse intrinsic DNA curvature within the gene. Thiswas achieved by developing a novel software platform for the atomic force microscopy basedinvestigation of DNA curvature, named ADIPAS. Dinucleotide wedge models of DNA curvaturewere used to model TP53 in order to investigate the relationship between intrinsic DNAcurvature and the structure and function of the gene. ADIPAS was applied to atomic forcemicroscopy images of TP53 DNA molecules immobilised on a mica surface in order toexperimentally measure intrinsic DNA curvature. The experimental findings were compared totheoretical models of intrinsic curvature in TP53. The resulting intrinsic curvature profilesshowed that exons exhibited significantly lower intrinsic DNA curvature than introns withinTP53, this was also shown to be true for regions of slow DNA repair. This indicated that DNAcurvature may play a role in TP53 as a controlling factor for nucleosomal architecture tofacilitate open chromatin and active DNA transcription. The evolutionary selection for intrinsiccurvature may have played a role in the development of exons with low intrinsic DNAcurvature. Low intrinsic curvature in exon position has also been implicated in the reducedefficiency of DNA repair in a number of cancer specific mutation hotspots

Particles in biopharmaceutical formulations, part 2: an update on analytical techniques and applications for therapeutic proteins, viruses, vaccines and cells

Particles in biopharmaceutical formulations remain a hot topic in drug product development. With new product classes emerging it is crucial to discriminate particulate active pharmaceutical ingredients from particulate impurities. Technical improvements, new analytical developments and emerging tools (e.g., machine learning tools) increase the amount of information generated for particles. For a proper interpretation and judgment of the generated data a thorough understanding of the measurement principle, suitable application fields and potential limitations and pitfalls is required. Our review provides a comprehensive overview of novel particle analysis techniques emerging in the last decade for particulate impurities in therapeutic protein formulations (protein-related, excipient-related and primary packaging material-related), as well as particulate biopharmaceutical formulations (virus particles, virus-like particles, lipid nanoparticles and cell-based medicinal products). In addition, we review the literature on applications, describe specific analytical approaches and illustrate advantages and drawbacks of currently available techniques for particulate biopharmaceutical formulations.Drug Delivery Technolog