Visualization and quantitative analyses for mouse embryonic stem cell tracking by manipulating hierarchical data structures using time-lapse confocal microscopy images.

We present a cell tracking method for time-lapse confocal microscopy (3D) images that uses dynamic hierarchical data structures to assist cell and colony segmentation and tracking. During the segmentation, the cell and colony numbers and their geometric data are recorded for each 3D image set. In tracking, the colony correspondences between neighboring frames of time-lapse 3D images are first computed using the recorded colony centers. Then, cell correspondences in the correspondent colonies are computed using the recorded cell centers. The examples show the proposed cell tracking method can achieve high tracking accuracy for time-lapse 3D images of undifferentiated but self-renewing mouse embryonic stem (mES) cells where the number and mobility of ES cells in a cell colony may change suddenly by a colony merging or splitting, and cell proliferation or death. The geometric data in the hierarchical data structures also help the visualization and quantitation of the cell shapes and mobility.

Multiple nucleic acid binding sites and intrinsic disorder of severe acute respiratory syndrome coronavirus nucleocapsid protein: implications for ribonucleocapsid protein packaging.

The nucleocapsid protein (N) of the severe acute respiratory syndrome coronavirus (SARS-CoV) packages the viral genomic RNA and is crucial for viability. However, the RNA-binding mechanism is poorly understood. We have shown previously that the N protein contains two structural domains--the N-terminal domain (NTD; residues 45 to 181) and the C-terminal dimerization domain (CTD; residues 248 to 365)--flanked by long stretches of disordered regions accounting for almost half of the entire sequence. Small-angle X-ray scattering data show that the protein is in an extended conformation and that the two structural domains of the SARS-CoV N protein are far apart. Both the NTD and the CTD have been shown to bind RNA. Here we show that all disordered regions are also capable of binding to RNA. Constructs containing multiple RNA-binding regions showed Hill coefficients greater than 1, suggesting that the N protein binds to RNA cooperatively. The effect can be explained by the "coupled-allostery" model, devised to explain the allosteric effect in a multidomain regulatory system. Although the N proteins of different coronaviruses share very low sequence homology, the physicochemical features described above may be conserved across different groups of Coronaviridae. The current results underscore the important roles of multisite nucleic acid binding and intrinsic disorder in N protein function and RNP packaging.

Prediction for Morphology and States of Stem Cell Colonies using a LSTM Network with Progressive Training Microscopy Images.

We present a new LSTM (P-LSTM: Progressive LSTM) network, aiming to predict morphology and states of cell colonies from time-lapse microscopy images. Apparent short-term changes occur in some types of time-lapse cell images. Therefore, long-term-memory dependent LSTM networks may not predict accurately. The P-LSTM network incorporates the images newly generated from cell imaging progressively into LSTM training to emphasize the LSTM short-term memory and thus improve the prediction accuracy. The new images are input into a buffer to be selected for batch training. For real-time processing, parallel computation is introduced to implement concurrent training and prediction on partitioned images.Two types of stem cell images were used to show effectiveness of the P-LSTM network. One is for tracking of ES cell colonies. The actual and predicted ES cell images possess similar colony areas and the same transitions of colony states (moving, merging or morphology changing), although the predicted colony mergers may delay in several time-steps. The other is for prediction of iPS cell reprogramming from the CD34+ human cord blood cells. The actual and predicted iPS cell images possess high similarity evaluated by the PSNR and SSIM similarity evaluation metrics, indicating the reprogramming iPS cell colony features and morphology can be accurately predicted.

Human Induced Pluripotent Stem Cell Reprogramming Prediction in Microscopy Images using LSTM based RNN.

We present a LSTM (Long Short-Term Memory) based RNN (recurrent neural network) method for predicting human induced Pluripotent Stem (hiPS) cells in the reprogramming process. The method uses a trained LSTM network by time-lapse microscopy images to predict growth and transition of reprogramming processes of CD34+ human cord blood cells into hiPS cells. The prediction can be visualized by output time-series probability images. The growth and transition are thus analyzed quantitatively by region areas of distinct cells emerged during the iPS formation processes. The experimental results show that our LSTM network is a potentially powerful tool to predict the cells at the distinct phases of the reprogramming to hiPS cells. This method should be extremely useful not only for basic biology of iPS cells but also detection of the reprogramming cells that will become genuine hiPS cells even at early stages of hiPS formation. Such predictive power should greatly reduce cost, labor and time required for establishment of the genuine hiPS cells, thereby accelerating the practical use of hiPS cells in regenerative medicine.

Integrated density of a lesion: a quantitative, mammographically derived, invariable measure.

A method for quantitatively estimating lesion "size" from mammographic images was developed and evaluated. The main idea behind the measure, termed "integrated density" (ID), is that the total x-ray attenuation attributable to an object is theoretically invariant with respect to the projected view and object deformation. Because it is possible to estimate x-ray attenuation of a lesion from relative film densities, after appropriate corrections for background, the invariant property of the measure is expected to result in an objective method for evaluating the "sizes" of breast lesions. ID was calculated as the integral of the estimated image density attributable to a lesion, relative to surrounding background, over the area of the lesion and after corrections for the nonlinearity of the film characteristic curve. This effectively provides a measure proportional to lesion volume. We computed ID and more traditional measures of size (such as "mass diameter" and "effective size") for 100 pairs of ipsilateral mammographic views, each containing a lesion that was relatively visible in both views. The correlation between values calculated for each measure from corresponding pairs of ipsilateral views were computed and compared. All three size-related measures (mass diameter, effective size, and ID) exhibited reasonable linear relationship between paired views (r2>0.7, P<0.001). Specifically, the ID measures for the 100 masses were found to be highly correlated (r2=0.9, P<0.001) between ipsilateral views of the same mass. The correlation increased substantially (r2=0.95), when a measure with linear dimensions of length was defined as the cube root of ID. There is a high degree of correlation between ID-based measures obtained from different views of the same mass. ID-based measures showed a higher degree of invariance than mass diameter or effective size.

Computerized assessment of tissue composition on digitized mammograms.

RATIONALE AND OBJECTIVES: The authors developed a computerized method for the quantitative assessment of breast tissue composition on digitized mammograms. MATERIALS AND METHODS: Three radiologists were asked to review 200 digitized mammograms and independently provide a Breast Imaging Reporting and Data System-like rating for breast tissue composition on a scale of 0 to 4. These values were incorporated into a "consensus" rating that was used as a reference point in the development and evaluation of a computerized method. After tissue segmentation that excluded nontissue areas, a set of quantitative features was computed. A computerized summary index that attempts to reproduce the radiologists' ratings was developed. Correlation coefficients (Pearson r) were used to compare the computerized index with the consensus ratings. RESULTS: Some individual features computed for the relatively dense breast areas showed good correlation (r > 0.8) with the radiologists' subjective ratings. The summary index of tissue composition demonstrated a significant correlation (r = 0.87), as well. CONCLUSION: Computerized methods that show good correlation with radiologists' ratings of breast tissue composition can be developed.

Solution structure of the c-terminal dimerization domain of SARS coronavirus nucleocapsid protein solved by the SAIL-NMR method.

The C-terminal domain (CTD) of the severe acute respiratory syndrome coronavirus (SARS-CoV) nucleocapsid protein (NP) contains a potential RNA-binding region in its N-terminal portion and also serves as a dimerization domain by forming a homodimer with a molecular mass of 28 kDa. So far, the structure determination of the SARS-CoV NP CTD in solution has been impeded by the poor quality of NMR spectra, especially for aromatic resonances. We have recently developed the stereo-array isotope labeling (SAIL) method to overcome the size problem of NMR structure determination by utilizing a protein exclusively composed of stereo- and regio-specifically isotope-labeled amino acids. Here, we employed the SAIL method to determine the high-quality solution structure of the SARS-CoV NP CTD by NMR. The SAIL protein yielded less crowded and better resolved spectra than uniform (13)C and (15)N labeling, and enabled the homodimeric solution structure of this protein to be determined. The NMR structure is almost identical with the previously solved crystal structure, except for a disordered putative RNA-binding domain at the N-terminus. Studies of the chemical shift perturbations caused by the binding of single-stranded DNA and mutational analyses have identified the disordered region at the N-termini as the prime site for nucleic acid binding. In addition, residues in the beta-sheet region also showed significant perturbations. Mapping of the locations of these residues onto the helical model observed in the crystal revealed that these two regions are parts of the interior lining of the positively charged helical groove, supporting the hypothesis that the helical oligomer may form in solution.

Automated assessment of the composition of breast tissue revealed on tissue-thickness-corrected mammography.

OBJECTIVE: Variations in the thickness of a compressed breast and the resulting variations in mammographic densities confound current automated procedures for estimating tissue composition of breasts from digitized mammograms. We sought to determine whether adjusting mammographic data for tissue thickness before estimating tissue composition could improve the accuracy of the tissue estimates. MATERIALS AND METHODS: We developed methods for locally estimating breast thickness from mammograms and then adjusting pixel values so that the values correlated with the tissue composition over the breast area. In our technique, the pixel values are corrected for the nonlinearity of the combined characteristic curve from the film and film digitizer; the approximate relative thickness as a function of distance from the skin line is measured; and the pixel values are adjusted to reflect their distance from the skin line. To estimate tissue composition, we created a backpropagation neural network classifier from features extracted from the histogram of pixel values, after the data had been adjusted for characteristic curve and tissue thickness. We used a 10-fold cross-validation method to evaluate the neural network. The averaged scores of three radiologists were our gold standard. RESULTS: The performance of the neural network was calculated as the percentage of correct classifications of images that were or were not corrected to reflect tissue thickness. With its parameters derived from the pixel-value histogram, the neural network based on corrected images performed better (71% accuracy) than that based on uncorrected images (67% accuracy) (p < 0.05). CONCLUSION: Our results show that adjusting tissue thickness before estimating tissue composition improved the performance of our estimation procedure in reproducing the tissue composition values determined by radiologists.