Automatic section thickness determination using an absolute gradient focus function

Technical Note: Measuring the thickness of histological sections by detecting fluorescence intensity of embedding foam

Fluorescence intensity of embedding foam in paraffin blocks can be used to measure the thickness of histological microsections. We embedded samples of embedding foam and produced several microsections of varying thicknesses using routine processing and staining. Fluorescence intensity in the blue area of the embedding foam detected with a slide scanner was compared to absolute thickness as measured using confocal microscopy. Correlation analysis displayed a clear linear correlation with convincingly low prediction interval. The concept of measuring thickness of histological microsections by detecting fluorescence intensity of embedding foam is suggested as an approach to high-throughput measuring of histological sections applicable for a fully digitized pathology department. No acquisition of dedicated equipment is required and the method can be applied as a fully automated technique requiring no time consumption.

A new method for physical disector analyses of numbers and mean volumes of immunohistochemically labeled cells in paraffin sections

BACKGROUND

In the neurosciences, the physical disector method represents an established quantitative stereological method for unbiased sampling and counting of cells in histological tissue sections of known thickness. Physical disector analyses are conventionally performed using plastic-embedded tissue samples, because plastic-embedding causes a comparably low and definable shrinkage of the embedded tissue, and the thickness of thin plastic sections can be determined adequately. However, immunohistochemistry protocols often don't work satisfactorily in sections of plastic-embedded tissue.

NEW METHOD

Here, a new methodological approach is presented, allowing for physical disector analyses of immunohistochemically labeled cells in paraffin sections. The embedding-related tissue shrinkage is standardized by using defined tissue sample volumes and paraffin volumes, and the extent of tissue shrinkage can be determined accurately from the sample volumes prior to and after embedding. Co-embedding of polyethylene section thickness standards together with the tissue samples allows the precise determination of individual paraffin section thicknesses by spectral reflectance measurements.

RESULTS AND COMPARISON WITH EXISTING METHOD(S)

The applicability of the new method is demonstrated by physical disector analysis of immunohistochemically identified somatotroph cells in paraffin sections of porcine pituitary gland tissue. With consideration of individual shrinkage factors and section thicknesses, the cell numbers and mean volumes estimated in paraffin disector sections do not significantly differ from the results obtained by analyses of plastic-embedded pituitary tissue samples of the identical animals (2.4% average difference).

CONCLUSIONS

The featured method enables combination of paraffin section immunohistochemistry and physical disector analyses for unbiased quantitative stereological analyses of different cell types.

Basic quantitative morphological methods applied to the central nervous system

Generating numbers has become an almost inevitable task associated with studies of the morphology of the nervous system. Numbers serve a desire for clarity and objectivity in the presentation of results and are a prerequisite for the statistical evaluation of experimental outcomes. Clarity, objectivity, and statistics make demands on the quality of the numbers that are not met by many methods. This review provides a refresher of problems associated with generating numbers that describe the nervous system in terms of the volumes, surfaces, lengths, and numbers of its components. An important aim is to provide comprehensible descriptions of the methods that address these problems. Collectively known as design-based stereology, these methods share two features critical to their application. First, they are firmly based in mathematics and its proofs. Second and critically underemphasized, an understanding of their mathematical background is not necessary for their informed and productive application. Understanding and applying estimators of volume, surface, length or number does not require more of an organizational mastermind than an immunohistochemical protocol. And when it comes to calculations, square roots are the gravest challenges to overcome. Sampling strategies that are combined with stereological probes are efficient and allow a rational assessment if the numbers that have been generated are "good enough." Much may be unfamiliar, but very little is difficult. These methods can no longer be scapegoats for discrepant results but faithfully produce numbers on the material that is assessed. They also faithfully reflect problems that associated with the histological material and the anatomically informed decisions needed to generate numbers that are not only valid in theory. It is within reach to generate practically useful numbers that must integrate with qualitative knowledge to understand the function of neural systems.

Practicable methods for histological section thickness measurement in quantitative stereological analyses

The accuracy of quantitative stereological analysis tools such as the (physical) disector method substantially depends on the precise determination of the thickness of the analyzed histological sections. One conventional method for measurement of histological section thickness is to re-embed the section of interest vertically to its original section plane. The section thickness is then measured in a subsequently prepared histological section of this orthogonally re-embedded sample. However, the orthogonal re-embedding (ORE) technique is quite work- and time-intensive and may produce inaccurate section thickness measurement values due to unintentional slightly oblique (non-orthogonal) positioning of the re-embedded sample-section. Here, an improved ORE method is presented, allowing for determination of the factual section plane angle of the re-embedded section, and correction of measured section thickness values for oblique (non-orthogonal) sectioning. For this, the analyzed section is mounted flat on a foil of known thickness (calibration foil) and both the section and the calibration foil are then vertically (re-)embedded. The section angle of the re-embedded section is then calculated from the deviation of the measured section thickness of the calibration foil and its factual thickness, using basic geometry. To find a practicable, fast, and accurate alternative to ORE, the suitability of spectral reflectance (SR) measurement for determination of plastic section thicknesses was evaluated. Using a commercially available optical reflectometer (F20, Filmetrics^®, USA), the thicknesses of 0.5 μm thick semi-thin Epon (glycid ether)-sections and of 1–3 μm thick plastic sections (glycolmethacrylate/ methylmethacrylate, GMA/MMA), as regularly used in physical disector analyses, could precisely be measured within few seconds. Compared to the measured section thicknesses determined by ORE, SR measures displayed less than 1% deviation. Our results prove the applicability of SR to efficiently provide accurate section thickness measurements as a prerequisite for reliable estimates of dependent quantitative stereological parameters.

Unbiased estimation of cell number using the automatic optical fractionator

A novel stereology approach, the automatic optical fractionator, is presented for obtaining unbiased and efficient estimates of the number of cells in tissue sections. Used in combination with existing segmentation algorithms and ordinary immunostaining methods, automatic estimates of cell number are obtainable from extended depth of field images built from three-dimensional volumes of tissue (disector stacks). The automatic optical fractionator is more accurate, 100% objective and 8-10 times faster than the manual optical fractionator. An example of the automatic fractionator is provided for counts of immunostained neurons in neocortex of a genetically modified mouse model of neurodegeneration. Evidence is presented for the often overlooked prerequisite that accurate counting by the optical fractionator requires a thin focal plane generated by a high optical resolution lens.

Early Expression of Parkinson's Disease-Related Mitochondrial Abnormalities in PINK1 Knockout Rats

PTEN-induced kinase 1 (PINK1) mutations are responsible for an autosomal recessive, familial form of Parkinson's disease. PINK1 protein is a Ser/Thr kinase localized to the mitochondrial membrane and is involved in many processes including mitochondrial trafficking, mitophagy, and proteasomal function. Using a new PINK1 knockout (PINK1 KO) rat model, we found altered brain metabolomic markers using magnetic resonance spectroscopy, identified changes in mitochondrial pathways with quantitative proteomics using sequential window acquisition of all theoretical spectra (SWATH) mass spectrometry, and demonstrated mitochondrial functional alterations through measurement of oxygen consumption and acidification rates. The observed alterations included reduced creatine, decreased levels of complex I of the electron transport chain, and increased proton leak in the electron transport chain in PINK1 KO rat brains. In conjunction, these results demonstrate metabolomic and mitochondrial alterations occur during the asymptomatic phase of Parkinson's disease in this model. These results indicate both potential early diagnostic markers and therapeutic pathways that can be used in PD.

Flexible focus function consisting of convex function and image enhancement filter

We propose a new focus function Λ that, like many of the existing focus functions, consists of a convex function and an image enhancement filter. Λ is rather flexible because for any convex function and image enhancement filter, it is a focus function. We proved that Λ is a focus function using a model and Jensen's inequality. Furthermore, we generated random Λs and experimentally applied them to simulated and real blurred images, finding that 98% and 99% of the random Λs, respectively, have a maximum value at the best-focused image and most of them decrease as the defocus increases. We also applied random Λs to motion-blurred images, blurred images in different-sized windows, and blurred images with different types of noise. We found that Λ can be applied to motion blur and is robust to different-sized windows and different noise types.