We present a cluster spatial analysis method using nanoscopic dSTORM images to determine changes in protein cluster distributions within brain tissue. Such methods are suitable to investigate human brain tissue and will help to achieve a deeper understanding of brain disease along with aiding drug development. Human brain tissue samples are usually treated postmortem via standard fixation protocols, which are established in clinical laboratories. Therefore, our localization microscopy-based method was adapted to characterize protein density and protein cluster localization in samples fixed using different protocols followed by common fluorescent immunohistochemistry techniques. The localization microscopy allows nanoscopic mapping of serotonin 5-HT1A receptor groups within a two-dimensional image of a brain tissue slice. These nanoscopically mapped proteins can be confined to clusters by applying the proposed statistical spatial analysis. Selected features of such clusters were subsequently used to characterize and classify the tissue. Samples were obtained from different types of patients, fixed with different preparation methods, and finally stored in a human tissue bank. To verify the proposed method, samples of a cryopreserved healthy brain have been compared with epitope-retrieved and paraffin-fixed tissues. Furthermore, samples of healthy brain tissues were compared with data obtained from patients suffering from mental illnesses (e.g., major depressive disorder). Our work demonstrates the applicability of localization microscopy and image analysis methods for comparison and classification of human brain tissues at a nanoscopic level. Furthermore, the presented workflow marks a unique technological advance in the characterization of protein distributions in brain tissue sections.
We developed a microarray analysis platform for ultra-sensitive RNA expression profiling of
minute samples. It utilizes a novel scanning system for single molecule fluorescence detection
on cm2 size samples in combination with specialized biochips, optimized for low
autofluorescence and weak unspecific adsorption.
20 μg total RNA was extracted from 106 cells of a human keratinocyte cell line (HaCaT) and reversely transcribed in the presence of Alexa647-aha-dUTP. 1% of the resulting labeled cDNA was used for complex hybridization to a custom-made oligonucleotide microarray representing a set of 125 different genes. For low abundant genes, individual cDNA molecules hybridized to the microarray spots could be resolved. Single cDNA molecules hybridized to the chip surface appeared as diffraction limited features in the fluorescence images. The à trous wavelet method was utilized for localization and counting of the separated cDNA signals. Subsequently, the degree of labeling of the localized cDNA molecules was determined by brightness analysis for the different genes. Variations by factors up to 6 were found, which in conventional microarray analysis would result in a misrepresentation of the relative abundance of mRNAs.
The in vivo imaging of proteins represents a promising technique for understanding the processes taking place at cellular level. Tracking the single proteins manually is tedious and the results are difficult to replicate. Due to the imaging characteristics the automation of the task is difficult. In this paper we study the problem of denoising of the image sequences, spot detection and data association. The 3D version of three denoising algorithms were implemented: adaptive mean filtering, anisotropic diffusion and spatial-tonal convolution. Their effect combined with the spot detection based on the à trous wavelet transform is studied. Finally, a point tracking algorithm is applied having as input the spots detected in the previous step. The algorithm can handle new track creation, track termination as well as one frame occlusions. The paper concludes with a discussion of the results and further work.