A method for determining ploidy distributions in liver tissue by stereological analysis of nuclear size calibrated by flow cytometric DNA analysis

A method is presented for determining ploidy distributions in mouse liver from image analysis with stereological estimations of nuclear size in tissue sections. Nuclear profile distributions obtained from profile measurements were subjected to a mathematical unfolding procedure in order to obtain the nuclear size distributions. Based on the assumption that nuclear size increases monotonically with nuclear DNA content, flow cytometric DNA analysis of suspensions of liver cell nuclei was used to calibrate the method, thus yielding the mean nuclear size of each ploidy class, i.e., diploid, tetraploid, and octaploid nuclei. After the size interval for each of the ploidy classes was determined, the method allowed determination of ploidy distributions in mouse liver by stereological image analysis alone. The method was established from combined stereological and flow cytometric measurements on liver tissue representing two different stages of liver regeneration after two-thirds partial hepatectomy, and it was tested against an independent set of data representing a marked increase in the portion of S-phase cells.     

Prediction of operator-binding protein by discriminant analysis

A number of operator-binding proteins contain similar sequence features to Cro and cI repressors of bacteriophage and CAP protein of Escherichia coli, such as conserved amino acids at constant positions. However, these sequence patterns also occur in proteins that are not operator-binding. We use sequence analogy information in conjunction with a pattern recognition algorithm. The functional and structural properties, e.g., distributions of hydrophobicity, hydrophilicity, charged amino acids, electrostatic free energy, and helical structures of protein are also considered. Within the framework of discriminant analysis, we calculate the above variables and search for a better combination of variables. To assess the discriminatory power of these variables, we allocated additional sequences and predict DNA-binding regions of regulatory proteins not included in the training set.     

Morphometric sex determination of young Ospreys Pandion haliaetus using discriminant analysis

Capsule Discriminant functions based on morphometric variables provide a reliable method for sex identification of free‐living and hacked young Ospreys.

Aims To describe an easy, accurate and low‐cost method for sex determination of fully grown nestling and fledgling Ospreys Pandion haliaetus based on morphometric measurements.

Methods Four different measurements were taken in 114 birds (40–73 days old) and a DNA analysis, using PCR amplification, was carried out for sex identification. A forward stepwise discriminant analysis was performed to build the best explanatory discriminant models, which were subsequently validated using statistics and external samples.

Results Our best discriminant function retained forearm and tarsus as the best predictor variables and classified 95.1% of the sample correctly, supported also by external cross‐validations with both hacked and free‐living birds. Moreover, a discriminant function with only forearm as predictor showed a similar high correct classification power (93.4%).

Conclusions These discriminant functions can be used as a reliable and immediate method for sex determination of young Ospreys since they showed high discriminant accuracy, close to that of molecular procedures, and were supported by external cross‐validations, both for free‐living and hacked birds. Thus, these morphometric measurements should be considered as standard tools for future scientific studies and management of Osprey populations     

Analytical methods for the study of liver cell proliferation.

Various cytometric methods for analysis of regenerating rat liver growth (DNA ploidy distributions, binucleation, and DNA synthesis by in vivo BrdUrd incorporation) were evaluated. The overall hepatocellular growth rate (labeling index), the binucleation rate, and separate indices for mononuclear and binuclear cells could be measured simply by microscope counting of collagenase-isolated hepatocytes immunostained for BrdUrd. Flow cytometry of cells stained for BrdUrd and DNA provided labeling indices for the various hepatocellular DNA ploidy classes as well as for nonparenchymal cells (identified by their size-dependent light scatter), but could not distinguish between mononuclear and binuclear hepatocytes. Image cytometry, using fluorescence or Feulgen staining, was inferior to flow cytometry in terms of speed and DNA resolution, but allowed a complete analysis of all hepatocellular DNA ploidy and nuclearity classes. It may therefore be the method of choice, particularly for analysis of liver cell cultures from which single cells are not easily obtained. Fluorescence staining would seem to be preferable to Feulgen staining, since the latter could not be used simultaneously with BrdUrd staining and therefore required a two-step analysis. A non-immunological method, based on the ability of incorporated BrdUrd to quench DNA staining by a Hoechst dye, could only be applied to isolated nuclei, thus giving no information about binucleation. The latter method may be useful for analysis of tumors which are difficult to dissociate to intact whole cells.     

Second order discriminant analysis in chemopreventive efficacy measurement

OBJECTIVE: To describe the use of second order discriminant analysis as a classification methodology along with the underlying assumptions and sampling requirements, with special emphasis on the use of this analysis in chemopreventive efficacy studies. STUDY DESIGN: The discriminant function score distributions derived in an analysis of 2 diagnostic groups may show such overlap that a statistically significant difference in mean values cannot be shown and, more important, that a useful case-based classification cannot be attained. By using the discriminant function score distributions from each case, it is frequently possible to derive a second order discriminant function based on case-specific characteristics, rather than characteristics of nuclei, thereby attaining improved case classification. RESULTS: Second order discriminant analysis has proven very useful in the documentation of case-level efficacy in chemopreventive trials. In a study of orally administered vitamin A, a first order discriminant analysis did not achieve a statistically significant difference in the score distributions for nuclei, but a second order discriminant analysis allowed a correct recognition of intervention effects in 85% of submitted cases. In a chemopreventive study of triamcinolone, a similarly inadequate discrimination based on discriminant function scores for nuclei resulted. After a second order discriminant analysis, a reduction in solar-actinic damage could be shown in 14/15, or 93%, of treated cases. CONCLUSION: Second order discriminant analysis can be highly effective when the discriminating information offered at the nuclear level is inadequate due to high dispersion and small differences in mean values of discriminant function scores for the diagnostic groups. Second order analysis utilizes case-specific characteristics of the discriminant function score distributions to document diagnostic group separation and/or efficacy of chemopreventive intervention by a reduction in case discriminant function scores.     

Robust discriminant analysis and its application to identify protein coding regions of rice genes

Identification of protein coding regions is fundamentally a statistical pattern recognition problem. Discriminant analysis is a statistical technique for classifying a set of observations into predefined classes and it is useful to solve such problems. It is well known that outliers are present in virtually every data set in any application domain, and classical discriminant analysis methods (including linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA)) do not work well if the data set has outliers. In order to overcome the difficulty, the robust statistical method is used in this paper. We choose four different coding characters as discriminant variables and an approving result is presented by the method of robust discriminant analysis.     

Preparatory methods for DNA hydrolysis, cytochemistry, immunocytochemistry and ploidy analysis. Their application to automated and routine diagnostic cytopathology

A review is presented of some methods used to prepare cytologic specimens for analytical and/or automated studies, with the steps of the procedures detailed in appendices. The preparation of the cell monolayers required for optimal automated cell image analysis and classification, e.g., by the Cytoscan 110, is discussed, as is the preparation of poly-L-lysine-coated slides used in the production of monolayered specimens. These monolayers, which can be prepared from a variety of specimens, are also useful for cytochemical and immunocytochemical studies and DNA ploidy analysis. For DNA analysis, a modified gallocyanin chrome alum staining procedure is described as a stoichiometric alternative to the time-consuming Feulgen reaction. The hydrolysis technique required by the latter method is also detailed. The freeze-fracturing technique for the enhancement of monoclonal antibody immunocytochemical staining of detectable antigens is described, along with an indirect immunoalkaline phosphatase staining method. The use of enzyme cytochemical reactions for glucose 6 phosphate dehydrogenase and lysosomal naphthylamidase is also presented.     

Comparison of ploidy level screening methods in Chinese dojo loach (Misgurnus anguillicaudatus)

In search for an easy, rapid and cost‐effective method to determine the ploidy levels of diploid and tetraploid dojo loaches Misgurnus anguillicaudatus distributed naturally in China, direct (karyotyping) and indirect (flow cytometry, erythrocyte nuclear measurements and morphometric analysis) methods were compared. The results revealed that all techniques employed may be successfully used to determine the ploidy levels. It was discovered that karyotyping is cumbersome; flow cytometry is expensive whereas erythrocyte nuclear measurement requires a long time and intensive labour. On the other hand, the morphometric analysis method, especially the measurement of head length (HL), snout length (SL) and depth of caudal peduncle (CPD), is the simplest, with no damage to the fish and can be considered a practical alternative to other techniques. The discriminant function developed from the specimens, D = 7.539(HL/CPD)?2.342(HL/SL)?5.636, categorized an observation as a diploid if the discriminant analysis gave a positive score, while negative scores were categorized as tetraploids.     

Erythrocyte nuclear measurements of diploid and triploid channel catfish, Ictalurus punctatus (Rafinesque)

Minor axis, major axis, and volume measurements of erythrocyte nuclei were compared by discriminant analysis to determine which variable best predicted ploidy levels. Predicted correct classification percentages for minor axis, volume, major axis, and all variables combined were 81·33, 86·21, 92·36 and 92·65%, respectively. Minor axis and volume measurements were not as accurate in predicting ploidy levels because nuclear shape changed as the ploidy level changed from the diploid to triploid state. Mean major axis measurements alone classified 92·36% of the fish correctly with a discriminant level of 4·726 μm.     

Comparison of quantitative histomorphometry and DNA ploidy in tissue sections of prostate carcinoma

OBJECTIVE: To examine the ability of quantitative histomorphometry to predict DNA ploidy of prostate carcinoma in biopsy tissue sections assigned after quantitation by nuclear digital image analysis. STUDY DESIGN: Thirty-five diploid, 35 tetraploid and 35 aneuploid prostatic carcinomas in biopsies, assessed by the CAS 200 image analyzer (Bacus Laboratories, Lombard, Illinois, U.S.A.), were reevaluated by the Bacus Laboratories Incorporated Slide Scanner, a microscope that quantifies histologic images. Thirty-one histomorphometric features from cancer cells were captured at 40 x magnification, averaged across tilesfor each case and incorporated into a multivariate discriminant model to determine which features predicted ploidy interpretation by nuclear image analysis using the CAS 200. RESULTS: On average, 60 and 15 minutes were required to perform nuclear image analysis and histomorphometry, respectively. The multivariate discriminant model identified configurable run length, difference variance, contrast, inverse difference moment, sum entropy and diagonal variance as histomorphometry features capable of distinguishing diploid from nondiploid tumors (P < .05). Cross-validation studies showed the model correctly classified 74.3% of the diploid and 57.1% of the nondiploid cases. CONCLUSION: Quantitative histomorphometry can predict the ploidy of prostate carcinoma in biopsy tissue sections. Quantitative histomorphometry has potential as a method of rapidly assessing DNA ploidy otherwise earmarked for nuclear image analysis, resulting in savings of time and expense.     

Using Museum Collections to Estimate Diversity Patterns along Geographical Gradients

Quantifying species-richness patterns along geographical gradients (typically latitude and elevation) has a long history in ecology and can be based on more-or-less complete censuses from a specified area (plot sampling), selective collection within a specified area (e.g. museum collections), or general information about species distributions (e.g. observations of extremes along the gradient, distribution maps). All these approaches require complete sampling to give the true richness in an area, but the richness pattern (i.e., the relative changes in richness along the gradient) may be estimated without complete sampling, although equal sampling between areas is necessary. This is relatively easy to do for fine-scale plot sampling, but rarely easy for other types of data. For data extracted from museum collections, a correct perception of the species richness pattern therefore depends on post-sampling treatment of data. Two commonly applied techniques for quantifying species richness patterns with these types of data are discussed, namely interpolation of species ranges and rarefaction. Such treatment may correct for unequal sampling in some instances, but may in other cases introduce artificial patterns. With incomplete sampling interpolation introduces an artificial humped pattern and rarefaction requires similar species abundance distributions to make unbiased comparisons among samples. One must therefore be cautious when applying these methods for estimating species richness patterns along geographical gradients.     

Mark-mark scatterplots improve pattern analysis in spatial plant ecology

Point process statistics provides valuable tools for many ecological studies, where ‘points’ are commonly determined to represent the locations of plants or animals and ‘marks’ are additional items such as species or size. In the statistical analysis of marked point patterns, various correlation functions are used such as the mark variogram or the mark correlation function. Often the interpretation of these functions is not easy and the non-spatial ecologist is in need of support. In order to make the analysis of spatial point patterns more accessible to ecologists, we introduced and tested a new graphical method, the mark-mark scatterplot. This plot visualises the marks of point pairs of inter-point distances r smaller than some small distance r_max. We tested the application of the mark-mark scatterplot by reconsidering three quite different tree patterns: a pattern of longleaf pine trees from the southern US which was strongly influenced by fires, a tropical tree pattern of the species Shorea congestiflora from Sri Lanka and a Scots pine pattern from Siberia (Russia). The new method yielded previously undetected cause-effect information on mark behaviour at short inter-point distances and thus improved the analysis with mark correlation functions as well as complemented the information they provided. We discovered important new correlations in clusters of trees at close proximity. The application of the mark-mark scatterplot will facilitate the interpretation of point process summary statistics and will make point process analysis more accessible to ecologists not specialized in point process statistics.     

Is agNOR and DNA ploidy analysis useful for evaluating thyroid neoplasms?

OBJECTIVE: To correlate the subjective AgNOR counting method and DNA content with histologic diagnoses of thyroid cancer and invasion. STUDY DESIGN: Eighty-one consecutive cases of thyroid carcinoma were selected for DNA and AgNOR analysis. The diagnoses were: papillary carcinoma (n = 40), follicular carcinoma (n = 31), Hürthle cell adenocarcinoma (n = 4), and undifferentiated carcinoma (n = 6). Seven normal thyroids were used as controls. DNA quantitative measurement was performed with Vidas 2.0 software (Kontron Bildanalyse, Munich, Germany) connected to an MPM 210 photometer microscope (Carl Zeiss, Oberkochen, Germany). The DNA index was obtained using histograms. Counting the NORs was performed by subjectively counting the NORs in 200 malignant cells. RESULTS: DNA ploidy analysis showed all Hürthle cell adenocarcinomas, 21 (67%)follicular tumors, 23 (57%) papillary tumors and 4 (67%) undifferentiated carcinomas to be aneuploid. DNA analysis correlated with histologic type of the tumor (p = 0.032). There was no statistical significance to the AgNOR counting variables studied. Statistical analysis showed correlation between ploidy and histologic diagnosis, but not AgNOR counting, to have prognostic value. CONCLUSION: DNA ploidy is more useful than subjective counting of NORs as an adjunct method for thyroid lesion analysis.     

Comparison of multivariate methods for the analysis of genetic resources and adaptation in Phytolacca dodecandra using RAPD

The extent of genetic differentiation among 17 Ethiopian populations (249 individuals) of Phytolacca dodecandra (Endod) sampled along altitudinal gradients that varied from 1600 to 3000 m was investigated using random amplified polymorphic DNA (RAPD). The populations were classified into three altitude groups: lowland (1600–2100 m), central-highland (2101–2500 m) and highland (2500–3000 m). Seventy polymorphic loci scored from 12 RAPD primers, singly or in combination with ecogeographical variables (altitude, longitude, latitude, temperature and rainfall), were used for principal component, discriminant, correlation, and stepwise multiple regression analyses. Principal component analysis (PCA) clearly differentiated lowland and the central-highland populations from those of the highlands independent of their geographical regions. Canonical discriminant analysis separated the lowland plants from those of the highlands with the central-highland plants being intermediate. Classificatory discriminant analysis corrected classification of 92.8% of the 249 plants into their respective three altitude groups. Multiple regression analysis identified a strong association between some RAPDs and altitude, temperature and rainfall, while the variation in most RAPDs was explained by combinations of the different ecogeographical variables. It is hypothesised that the different altitude groups may be (1) chemical and/or physiological ecotypes produced as a result of complex interactions of altitude with climatic and/or edaphic factors, or (2) different in ploidy levels. The significant correlations obtained between population means from some RAPDs and altitude and temperature as well as the strong association of some RAPDs with the ecogeographical variables in the multiple regression analysis suggest that part of the RAPD polymorphism could be adaptive, and responsive to environmental selection. Received: 15 December 1999 / Accepted: 12 February 2000     

Intratumoral variations in DNA ploidy and s-phase fraction in human breast cancer.

To study intratumoral DNA ploidy heterogeneity and S-phase fraction (SPF) variability, we prospectively collected five different samples from 48 breast carcinomas and each sample was analysed separately by flow cytometry. Aneuploidy rate was 89.6% after analysis of four or five samples. DNA ploidy heterogeneity, i.e., different samples classified as either DNA euploid or DNA aneuploid in the same tumor was seen in 17%, and DNA index heterogeneity, i.e., tumor populations with different DNA indices (DIs) seen in different samples was 44%. A statistical model defining SPF heterogeneity is proposed. SPF heterogeneity as defined by us was 71%, and as expected the SPF heterogeneity rate increased significantly with increasing number of analysed samples. Four or more samples are needed to detect the most deviant (highest) SPF values. An unrecognized intratumor heterogeneity of DNA ploidy and SPF may partly explain the conflicting results reported in the literature on the above prognostic indicators.     

DNA ploidy analysis of cervical condyloma and intraepithelial neoplasia in specimens obtained by punch biopsy

This study analyzed the feasibility of, and the strategy for, DNA ploidy analysis of cervical condyloma and intraepithelial neoplasia by a computerized digital imaging system. Paraffin-embedded tissue provided satisfactory single-cell preparations for DNA ploidy analysis after enzyme digestion and additional procedures. Negative endocervical curettings and normal squamous mucosa were used as internal diploid controls. With suitable controls, 21 (81%) of the 26 aneuploid lesions were identified as such in the single-cell preparations. The remaining five lesions (not recognized as aneuploid in the single-cell preparations) had ploidy levels between 2.08n and 2.30n and required DNA measurements on 12-microns sections. Criteria for these DNA measurements were defined: specimens intended for DNA ploidy analysis should contain abnormal epithelium of at least 3 mm to 4 mm in greatest dimension and should be accompanied by diploid controls, such as endocervical curettings or normal ectocervical squamous mucosa. With a combination of single-cell preparations and 12-microns tissue sections, it was possible to accurately determine the DNA ploidy patterns of the cervical lesion specimens obtained by punch biopsies. Available evidence suggests that ploidy analysis can provide useful diagnostic and prognostic information.     

In planta quantification of endoreduplication using fluorescent in situ hybridization (FISH)

Endopolyploidy, i.e. amplification of the genome in the absence of mitosis, occurs in many plant species and happens along with organ and cell differentiation. Deciphering the functional roles of endopolyploidy is hampered by the fact that polyploid tissues generally comprise cells with various ploidy levels. In some fleshy fruits (amongst them tomato fruit) the ploidy levels present at the end of development range from 2C to 256C in the same tissue. To investigate the temporal and spatial distribution of endopolyploidy it is necessary to address the DNA content of individual nuclei in situ. Conventional methods such as fluorometry or densitometry can be used for some tissues displaying favorable characteristics, e.g. small cells, small nuclei, organization in a monolayer, but high levels of varying polyploidy are usually associated with large sizes of nuclei and cells, in a complex three dimensional (3-D) organization of the tissues. The conventional methods are inadequate for such tissue, becoming semi-quantitative and imprecise. We report here the development of a new method based on fluorescent in situ bacterial artificial chromosome hybridizations that allows the in situ determination of the DNA ploidy level of individual nuclei. This method relies on the counting of hybridization signals and not on intensity measurements and is expected to provide an alternative method for mapping endopolyploidy patterns in mature, 3-D organized plant tissues as illustrated by the analysis of ploidy level and cell size in pericarp from mature green tomato fruit.     

Nonlinear biological batch process monitoring and fault identification based on kernel fisher discriminant analysis

A novel nonlinear biological batch process monitoring and fault identification approach based on kernel Fisher discriminant analysis (kernel FDA) is proposed. This method has a powerful ability to deal with nonlinear data and does not need to predict the future observations of variables. So it is more sensitive to fault detection. In order to improve the monitoring performance, variable trajectories of the batch processes are separated into several blocks. Then data in the original space is mapped into high-dimensional feature space via nonlinear kernel function and the optimal kernel Fisher feature vector and discriminant vector are extracted to perform process monitoring and fault identification. The key to the proposed approach is to calculate the distance of block data which are projected to the optimal kernel Fisher discriminant vector between new batch and reference batch. Through comparing distance with the predefined threshold, it can be considered whether the batch is normal or abnormal. Similar degree between the present discriminant vector and the optimal discriminant vector of fault in historical data set is used to perform fault diagnosis. The proposed method is applied to the process of fed-batch penicillin fermentation simulator benchmark and shows that it can effectively capture nonlinear relationships among process variables and is more efficient than MPCA approach.     

Analyzing ion distributions around DNA

We present a new method for analyzing ion, or molecule, distributions around helical nucleic acids and illustrate the approach by analyzing data derived from molecular dynamics simulations. The analysis is based on the use of curvilinear helicoidal coordinates and leads to highly localized ion densities compared to those obtained by simply superposing molecular dynamics snapshots in Cartesian space. The results identify highly populated and sequence-dependent regions where ions strongly interact with the nucleic and are coupled to its conformational fluctuations. The data from this approach is presented as ion populations or ion densities (in units of molarity) and can be analyzed in radial, angular and longitudinal coordinates using 1D or 2D graphics. It is also possible to regenerate 3D densities in Cartesian space. This approach makes it easy to understand and compare ion distributions and also allows the calculation of average ion populations in any desired zone surrounding a nucleic acid without requiring references to its constituent atoms. The method is illustrated using microsecond molecular dynamics simulations for two different DNA oligomers in the presence of 0.15 M potassium chloride. We discuss the results in terms of convergence, sequence-specific ion binding and coupling with DNA conformation.     

How reproducible are flow cytometry data from paraffin-embedded blocks?

The purpose of this technical report is to determine the reproducibility of flow cytometry data for ploidy and cell cycle kinetics using paraffin-embedded blocks of breast cancer tissue. One block from each of 39 tumors was studied in this report with each block having multiple sections analyzed independently. All of these sections gave ploidy analyses, while only 34 gave cell kinetic values. The standard deviation for the DNA index value in the multiple analysis study was less than 0.1 in all but three cases. The cell kinetic values gave larger variability, and the actual values were dependent on the method of analysis. Comparison of the variability for each method of analysis could not predict which procedure was superior. These results would indicate that ploidy is a reproducible value, while cell kinetic parameters should only be used as an indicator of proliferative activity that has been normalized to the mean or median of a large set of observations processed and analyzed by the same procedure.