Nuclear Shape Analysis Research Articles

Sensitivity of magnetic resonance elastography to extracellular matrix and cell motility in human prostate cancer cell line-derived xenograft models

Prostate cancer (PCa) is a significant health problem in the male population of the Western world. Magnetic resonance elastography (MRE), an emerging medical imaging technique sensitive to mechanical properties of biological tissues, detects PCa based on abnormally high stiffness and viscosity values. Yet, the origin of these changes in tissue properties and how they correlate with histopathological markers and tumor aggressiveness are largely unknown, hindering the use of tumor biomechanical properties for establishing a noninvasive PCa staging system.To infer the contributions of extracellular matrix (ECM) components and cell motility, we investigated fresh tissue specimens from two PCa xenograft mouse models, PC3 and LNCaP, using magnetic resonance elastography (MRE), diffusion-weighted imaging (DWI), quantitative histology, and nuclear shape analysis. Increased tumor stiffness and impaired water diffusion were observed to be associated with collagen and elastin accumulation and decreased cell motility. Overall, LNCaP, while more representative of clinical PCa than PC3, accumulated fewer ECM components, induced less restriction of water diffusion, and exhibited increased cell motility, resulting in overall softer and less viscous properties. Taken together, our results suggest that prostate tumor stiffness increases with ECM accumulation and cell adhesion - characteristics that influence critical biological processes of cancer development.MRE paired with DWI provides a powerful set of imaging markers that can potentially predict prostate tumor development from benign masses to aggressive malignancies in patients. Statement of significanceXenograft models of human prostate tumor cell lines, allowing correlation of microstructure-sensitive biophysical imaging parameters with quantitative histological methods, can be investigated to identify hallmarks of cancer.

InShaDe: Invariant Shape Descriptors for visual 2D and 3D cellular and nuclear shape analysis and classification

We present a shape processing framework for visual exploration of cellular nuclear envelopes extracted from microscopic images arising in histology and neuroscience. The framework is based on a novel shape descriptor of closed contours in 2D and 3D. In 2D, it relies on a geodesically uniform resampling of discrete curves to compute unsigned curvatures at vertices and edges based on discrete differential geometry. Our descriptor is, by design, invariant under translation, rotation, and parameterization. We achieve the latter invariance under parameterization shifts by using elliptic Fourier analysis on the resulting curvature vectors. Uniform scale-invariance is optional and is a result of scaling curvature features to z-scores. We further augment the proposed descriptor with feature coefficients obtained through sparse coding of the extracted cellular structures using K-sparse autoencoders. For the analysis of 3D shapes, we compute mean curvatures based on the Laplace-Beltrami operator on triangular meshes, followed by computing a spherical parameterization through mean curvature flow. Finally, we compute the Spherical Harmonics decomposition to obtain invariant energy coefficients. Our invariant descriptors provide an embedding into a fixed-dimensional feature space that can be used for various applications, e.g., as input features for deep and shallow learning techniques or as input for dimension reduction schemes to provide a visual reference for clustering shape collections. We demonstrate the capabilities of our framework in the context of visual analysis and unsupervised classification of 2D histology images and 3D nuclear envelopes extracted from serial section electron microscopy stacks.

Three-dimensional analysis of nuclear shape in aortic smooth muscle cells during uniaxial stretching

Three-dimensional analysis of nuclear shape in aortic smooth muscle cells during uniaxial stretching

High-content image informatics of the structural nuclear protein NuMA parses trajectories for stem/progenitor cell lineages and oncogenic transformation

Stem and progenitor cells that exhibit significant regenerative potential and critical roles in cancer initiation and progression remain difficult to characterize. Cell fates are determined by reciprocal signaling between the cell microenvironment and the nucleus; hence parameters derived from nuclear remodeling are ideal candidates for stem/progenitor cell characterization. Here we applied high-content, single cell analysis of nuclear shape and organization to examine stem and progenitor cells destined to distinct differentiation endpoints, yet undistinguishable by conventional methods. Nuclear descriptors defined through image informatics classified mesenchymal stem cells poised to either adipogenic or osteogenic differentiation, and oligodendrocyte precursors isolated from different regions of the brain and destined to distinct astrocyte subtypes. Nuclear descriptors also revealed early changes in stem cells after chemical oncogenesis, allowing the identification of a class of cancer-mitigating biomaterials. To capture the metrology of nuclear changes, we developed a simple and quantitative “imaging-derived” parsing index, which reflects the dynamic evolution of the high-dimensional space of nuclear organizational features. A comparative analysis of parsing outcomes via either nuclear shape or textural metrics of the nuclear structural protein NuMA indicates the nuclear shape alone is a weak phenotypic predictor. In contrast, variations in the NuMA organization parsed emergent cell phenotypes and discerned emergent stages of stem cell transformation, supporting a prognosticating role for this protein in the outcomes of nuclear functions.

Quantitative analysis of nuclear shape in oral squamous cell carcinoma is useful for predicting the chemotherapeutic response.

The number of people afflicted with oral carcinoma in Japan has increased in recent years. Although preoperative neoadjuvant therapy with cisplatin and 5-fluorouracil are performed, chemotherapeutic response varies widely among the patients. With the aim of establishing novel indices to predict the therapeutic response to chemotherapy, we investigated the relationship between morphological features of pre-treatment oral carcinoma nuclei and the chemotherapeutic response using quantifying morphology of cell nuclei in pathological specimen images. We measured 4 morphological features of the nucleus of oral squamous cell carcinoma cases classified by the response to chemotherapy: No Change (NC) group, Partial Response (PR) group and Complete Response (CR) group. Furthermore, we performed immunohistochemical staining for p53 and Ki67 and calculated their positive rates in cancer tissues. Compactness and symmetry of the nucleus were significantly higher and nuclear edge response was significantly lower in cancer cells with lower chemotherapeutic responses compared high chemotherapeutic responders. As for positive rates of p53 and Ki67, there were no significant differences between any of the response groups. Morphological features of cancer cell nuclei in pathological specimens are sensitive predictive factors for the chemotherapeutic response to oral squamous cell carcinoma.

Automated image analysis of nuclear shape: what can we learn from a prematurely aged cell?

The premature aging disorder, Hutchinson-Gilford progeria syndrome (HGPS), is caused by mutant lamin A, which affects the nuclear scaffolding. The phenotypic hallmark of HGPS is nuclear blebbing. Interestingly, similar nuclear blebbing has also been observed in aged cells from healthy individuals. Recent work has shown that treatment with rapamycin, an inhibitor of the mTOR pathway, reduced nuclear blebbing in HGPS fibroblasts. However, the extent of blebbing varies considerably within each cell population, which makes manual blind counting challenging and subjective. Here, we show a novel, automated and high throughput nuclear shape analysis that quantitatively measures curvature, area, perimeter, eccentricity and additional metrics of nuclear morphology for large populations of cells. We examined HGPS fibroblast cells treated with rapamycin and RAD001 (an analog to rapamycin). Our analysis shows that treatment with RAD001 and rapamycin reduces nuclear blebbing, consistent with blind counting controls. In addition, we find that rapamycin treatment reduces the area of the nucleus, but leaves the eccentricity unchanged. Our nuclear shape analysis provides an unbiased, multidimensional "fingerprint" for a population of cells, which can be used to quantify treatment efficacy and analyze cellular aging.

Deformation‐based nuclear morphometry: Capturing nuclear shape variation in HeLa cells

The empirical characterization of nuclear shape distributions is an important unsolved problem with many applications in biology and medicine. Numerous genetic diseases and cancers have alterations in nuclear morphology, and methods for characterization of morphology could aid in both diagnoses and fundamental understanding of these disorders. Automated approaches have been used to measure features related to the size and shape of the cell nucleus, and statistical analysis of these features has often been performed assuming an underlying Euclidean (linear) vector space. We discuss the difficulties associated with the analysis of nuclear shape in light of the fact that shape spaces are nonlinear, and demonstrate methods for characterizing nuclear shapes and shape distributions based on spatial transformations that map one nucleus to another. By combining large deformation metric mapping with multidimensional scaling we offer a flexible approach for elucidating the intrinsic nonlinear degrees of freedom of a distribution of nuclear shapes. More specifically, we demonstrate approaches for nuclear shape interpolation and computation of mean nuclear shape. We also provide a method for estimating the number of free parameters that contribute to shape as well as an approach for visualizing most representative shape variations within a distribution of nuclei. The proposed methodology can be completely automated, is independent of the dimensionality of the images, and can handle complex shapes. Results obtained by analyzing two sets of images of HeLa cells are shown. In addition to identifying the modes of variation in normal HeLa nuclei, the effects of lamin A/C on nuclear morphology are quantitatively described.

Lamins A and C but Not Lamin B1 Regulate Nuclear Mechanics

Mutations in the nuclear envelope proteins lamins A and C cause a broad variety of human diseases, including Emery-Dreifuss muscular dystrophy, dilated cardiomyopathy, and Hutchinson-Gilford progeria syndrome. Cells lacking lamins A and C have reduced nuclear stiffness and increased nuclear fragility, leading to increased cell death under mechanical strain and suggesting a potential mechanism for disease. Here, we investigated the contribution of major lamin subtypes (lamins A, C, and B1) to nuclear mechanics by analyzing nuclear shape, nuclear dynamics over time, nuclear deformations under strain, and cell viability under prolonged mechanical stimulation in cells lacking both lamins A and C, cells lacking only lamin A (i.e. "lamin C-only" cells), cells lacking wild-type lamin B1, and wild-type cells. Lamin A/C-deficient cells exhibited increased numbers of misshapen nuclei and had severely reduced nuclear stiffness and decreased cell viability under strain. Lamin C-only cells had slightly abnormal nuclear shape and mildly reduced nuclear stiffness but no decrease in cell viability under strain. Interestingly, lamin B1-deficient cells exhibited normal nuclear mechanics despite having a significantly increased frequency of nuclear blebs. Our study indicates that lamins A and C are important contributors to the mechanical stiffness of nuclei, whereas lamin B1 contributes to nuclear integrity but not stiffness.

LACK OF ASSOCIATION OF PROSTATE CARCINOMA NUCLEAR GRADING WITH PROSTATE SPECIFIC ANTIGEN RECURRENCE AFTER RADICAL PROSTATECTOMY

Grading prostate cancer using the Gleason system relies only on architectural tumor growth, in contrast to other systems, such as the WHO system, which grade prostate carcinoma based on nuclear features as well as architectural patterns. The prognostic significance of nuclear grading remains controversial since most studies were performed before prostate specific antigen (PSA) screening became widely available. We evaluated the significance of nuclear grade for predicting PSA recurrence in a contemporary cohort of patients treated with radical prostatectomy for clinically localized prostate carcinoma. Nuclear grades 1 to 3 were determined in 141 consecutive radical prostatectomies in 1995. Predominant and worst nuclear grade was determined by a consensus of 3 pathologists. Statistical analysis compared nuclear grade with Gleason score using the chi-square test. The Cox proportional hazards analysis was performed to calculate the ability of nuclear grade, Gleason score and other variables to predict PSA recurrence. We identified a significant association of Gleason score with worst nuclear grade (p = 0.007). All 6 cases with a Gleason score of 8 or greater had a worst nuclear grade of 3, in contrast to 36 of 60 (60%) with a score 6 or less, in which the worst nuclear grade was 3. Of the 141 patients 31 (21.9%) had PSA recurrence at a median followup of 3.7 years. The univariate Cox model revealed significant associations of PSA recurrence with Gleason score 8 or greater (hazards ratio 5.5, p = 0.005), extraprostatic extension (hazards ratio 3.4, p = 0.001), positive surgical margin (hazards ratio 2.6, p = 0.009), seminal vesicle involvement (hazards ratio 7.3, p <0.001), preoperative serum PSA (hazards ratio 1.03, p = 0.007), tumor stage (hazards ratio 3.6, p = 0.001) and maximal tumor dimension (hazards ratio 2.4, p <0.001). However, overall and worst nuclear grade did not predict PSA recurrence (p = 0.89 and 0.13, respectively). Nuclear grade did not fit any multivariate model tested, which otherwise included Gleason score, log(PSA), surgical margin status, extraprostatic extension, seminal vesicle status, tumor size and pathological stage. By varying sample fixation time we also showed that benign prostate tissue in the same section as prostate carcinoma had grade 2 or 3 nuclear changes, that is moderate to marked anaplasia. High nuclear grade is associated with high Gleason score. However, prostate carcinoma with a Gleason score of 6 or less shows extreme variability. Nuclear grade determined by light microscopy failed to predict PSA recurrence in a contemporary series of men with clinically localized prostate cancer treated with radical prostatectomy. Nuclear morphology is subject to tissue fixation and processing artifact. Any nuclear morphometric study must consider this artifact.

Nuclear shape analysis for the assessment of local invasion and metastases in clinically localized prostate carcinoma

Nuclear shape analysis of histologic sections from radical prostatectomy specimens has retrospectively predicted outcome in patients with clinically localized prostate carcinoma. If outcome could be predicted preoperatively by nuclear shape analysis, patients might be selected better for definitive surgical therapy. Morphometric analysis of preoperative biopsies, however, has not correlated positively with values obtained from analysis of prostatectomy specimens. The nuclear shapes of histologic specimens of 20 organ-confined carcinomas, 10 periprostatic fat-invasive carcinomas, 10 seminal vesicle-invasive carcinomas, and 12 lymph node-metastatic carcinomas from 52 patients who had undergone radical prostatectomy for clinically localized disease were evaluated. Nuclei from areas of extraprostatic invasion or regional lymph node metastases were less round than those from the corresponding intraprostatic portion of the tumor (nuclear roundness factor (mean +/- SD) PPF, 51.2 +/- 3.1 vs. 31.2 +/- 3.2; SV, 52.4 +/- 4.1 vs. 31.6 +/- 2.5; and LN, 57.3 +/- 3.1 vs. 36.4 +/- 1.8; paired Student's t tests, P < 0.001). Cells sampled from the periphery of organ-confined tumors had a greater nuclear roundness factor (49.1 +/- 1.5) than did those sampled from the center (34.5 +/- 2.0; P < 0.001) or randomly throughout the tumor (37.8 +/- 1.6; P < 0.001). Nuclear roundness factors for all extraprostatic tumor foci and for peripheral tumor cells in organ-confined disease were similar (analysis of variance, P > 0.05). The intraprostatic portions of randomly sampled primary tumors had similar nuclear roundness factors, regardless of pathologic stage (P > 0.05). Among organ-confined carcinomas, nuclear shape was unrelated to tumor volume. Pathologic stage in clinically localized prostate carcinoma cannot be determined by the nuclear shape profiles of intraprostatic tumor cells. Thus, patients with a poor prognosis or high pathologic stage can be recognized only when samples for morphometric analysis include high proportions of nuclei from the extra-prostatic carcinoma and nuclei from the periphery of organ-confined carcinoma that may not be sampled routinely by prostate biopsy.

Nuclear Shape Analysis for Assessment of Prognosis in Renal Cell Carcinoma

Clinically localized renal cell carcinoma is cured by radical nephrectomy in 47% [stage T3a (II)] to 65% [stage T1, T2 (I)] of the patients. Local recurrence and metastatic disease probably result from undetectable microscopic metastases present at operation. Chemotherapy and immunotherapy may improve cure rates if administered adjuvantly. The outcome of individual patients who share surgical stage cannot be predicted reliably by tumor histology, pathological grading and/or nuclear deoxyribonucleic acid analysis.Two groups of 10 patients with clinically localized renal cell carcinoma were similar by sex distribution (5 men and 5 women), surgical stage (stages T1 in 1, T2 in 6 and T3a in 3 patients) and age (54.3 ± 15.2 standard deviation versus 55.8 ± 8.7 years). Group 1 had no recurrences with a minimum followup of 5 years and a mean followup of 10 years. Group 2 died of metastatic renal cell carcinoma after a mean of 5 years. All neoplastic areas of each paraffin-embedded operative specimen were randomly sampled and the nuclear perimeter of 150 cancerous cells was digitized. There were 25 shape descriptors calculated for each nucleus. All shape descriptors for each patient were described by 19 statistical tests. Nuclear perimeter and area as well as mean nuclear roundness factor failed to separate the 2 groups. Range median quartiles of ellipticities by Fourier analysis, coefficients of variation of chain code minimums and relative means of largest 10 convexity values produced greatest separation (Mann-Whitney-Wilcoxon test p less than 0.001, and variance normalized difference 3.21, 3.29 and 2.83, respectively). These descriptors normalized and summed provided near perfect separation (Mann-Whitney-Wilcoxon test p less than 0.001 and variance normalized difference 3.59).We developed a quantitative nuclear morphometric analysis system that permitted the correct assignment of outcome in 19 of 20 patients. Accurate prediction of prognosis in patients with clinically localized renal cell carcinoma by nuclear shape analysis may allow for selection of patients for adjuvant therapy who have clinically undetectable metastatic disease.

Nuclear Roundness Factor Measurement for Assessment of Prognosis of Patients With Prostatic Carcinoma. I. Testing Of A Digitization System

Standard pathological grading systems for prostatic carcinoma based upon glandular architectural pattern or nuclear anaplasia have failed to predict the prognosis of individual patients. Quantitative morphometric analysis of nuclear shape has predicted the outcome of patients with prostatic carcinoma when evaluated by some but not all reported studies. Calculation of nuclear roundness factor by different investigators was complicated by equipment differences and lack of standardized methods for acquiring and reporting data. We have improved our system for nuclear contour digitization and determined its theoretical limitations by digitizing standardized objects. Measurement errors were independent of orientation or location of an object within a microscopic or digitizer tablet field, speed of digitization and introduction of a microscope extension tube and light emitting diode cursor. Perimeters and areas of circles and squares of actual or digitizer-projected diameter or sidelength greater than five mm. were measured with a reproducibility and accuracy of greater than or equal to 90 %. When a microscopic circle of diameter similar to prostatic carcinoma nuclei was digitized at a magnification of 2580 X perimeter and area measurements differed within or between observers by less than 5 % and were more than 95% accurate. In order to calculate accurately and evaluate NRF for use in assessing the prognosis of patients with prostatic carcinoma investigators must precisely describe their digitization system, standardization method and observer reproducibility and accuracy when measuring circles that approximate the projected size of prostatic carcinoma nuclei.

Nuclear roundness factor. A predictor of progression in untreated Stage A2 prostate cancer.

A measurement of nuclear roundness involving computer-assisted image analysis of histologic specimens was used to predict the prognosis of 19 patients with Stage A2 prostate cancer. This technique accurately identified those tumors that clinically progressed without treatment and those that did not, in contrast to the Gleason grading system, which produced significant prognostic overlap. What separated most of these tumors was the quantification and degree of irregularity of a relatively small subpopulation of cancer nuclei among a majority of rounder nuclei. There are currently several practical limitations in the use of this method, but with improvements in technique and in methods of obtaining tissue, computerized image analysis of nuclear shape may become a valuable tool for analyzing prostate cancer in a quantitative manner, thereby providing valuable information relative to the prognosis of the individual patient.

Morphometry in pathology

As medical sciences strive to make observations more precise, pathologists are becoming interested in objective histological and cytological evaluation. Morphometry is one procedure for deriving objective data, and measurements and statistical analysis of cell, nuclear or tissue parameters can be made with relatively inexpensive equipment. We are currently using an Apple II Plus microcomputer, a digitizing tablet (GTCO Corporation, Rockville, Md.) and a morphometric analysis system software diskette (Atlantis Scientific Systems Group Inc., Ottawa, Canada).Increasing numbers of reports of morphometric studies in pathology are appearing in the literature. Comparative analysis of nuclear shape of lymphocytes in mycosis fungoides and chronic dermatoses and measurements of microvillous length in mesotheliomas and adenocarcinomas show the value of this technique.

Computerized image analysis of nuclear shape as a prognostic factor for prostatic cancer.

Computer assisted image analysis was used to determine quantitative and reproducible nuclear morphology of 17 totally excised pathological specimens from stage B-2 prostatic cancer in an attempt to correlate histopathological analysis with clinical prognosis for the individual patient. We describe a nuclear roundness factor that correlates with the clinical outcome of the individual patient. This nuclear shape factor appears to identify prostatic tumors with a high metastatic potential from tumors that are less aggressive. Quantitative nuclear image analysis, its accuracy, and reproducibility are described and discussed. This technique may provide the pathologist with a valuable tool for analyzing prostatic cancer cells in a quantitative manner, thereby contributing precise information to the urologist relevant to the prognosis of the individual patient.