Sudeshna Adak

Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue

Limitations on the number of unique protein and DNA molecules that can be characterized microscopically in a single tissue specimen impede advances in understanding the biological basis of health and disease. Here we present a multiplexed fluorescence microscopy method (MxIF) for quantitative, single-cell, and subcellular characterization of multiple analytes in formalin-fixed paraffin-embedded tissue. Chemical inactivation of fluorescent dyes after each image acquisition round allows reuse of common dyes in iterative staining and imaging cycles. The mild inactivation chemistry is compatible with total and phosphoprotein detection, as well as DNA FISH. Accurate computational registration of sequential images is achieved by aligning nuclear counterstain-derived fiducial points. Individual cells, plasma membrane, cytoplasm, nucleus, tumor, and stromal regions are segmented to achieve cellular and subcellular quantification of multiplexed targets. In a comparison of pathologist scoring of diaminobenzidine staining of serial sections and automated MxIF scoring of a single section, human epidermal growth factor receptor 2, estrogen receptor, p53, and androgen receptor staining by diaminobenzidine and MxIF methods yielded similar results. Single-cell staining patterns of 61 protein antigens by MxIF in 747 colorectal cancer subjects reveals extensive tumor heterogeneity, and cluster analysis of divergent signaling through ERK1/2, S6 kinase 1, and 4E binding protein 1 provides insights into the spatial organization of mechanistic target of rapamycin and MAPK signal transduction. Our results suggest MxIF should be broadly applicable to problems in the fields of basic biological research, drug discovery and development, and clinical diagnostics.

Predicting the rate of cognitive decline in aging and early Alzheimer disease

Objectives: To determine prognostic factors affecting the course of Alzheimer disease (AD) and to determine the role of region-specific brain volumes as predictors of cognitive decline. Methods: Longitudinal data from 166 normal elderly individuals and 59 early AD patients were analyzed. Brain volumes were extracted from MRI scans using semiautomated recursive segmentation methods. Prognostic factors were considered significant if they had a significant effect on the rate of cognitive decline. Results: In multivariate analysis, higher Clinical Dementia Rating Scale (CDR) score at entry was a significant prognostic factor for an increased rate of cognitive decline. Significant prognostic factors within the baseline CDR = 0 group were base rate of progression and percent total high signal intensity (HSI), percent ventricular, and percent CSF volumes. Base rate of progression, family history, and percent ventricular volume were significant prognostic factors within the CDR = 0.5 group and APOE had a marginally significant effect on the rate of cognitive decline in the CDR = 1 group. Conclusions: Percent total HSI, ventricular, and total CSF volume measures can independently predict the rate of cognitive decline and improve the predictive power of statistical models that use only clinical data. Brain volumetric measures from MRI can be used to estimate the rate of cognitive decline even among normal elderly individuals and thus may aid in the prediction of time of onset of disease.

High-field magnetic resonance imaging of brain iron in Alzheimer disease.

Objectives: Increased iron deposition in the brain may occur in several neurodegenerative diseases, including Alzheimer disease (AD). Iron deposits shorten T2 relaxation times on T2-weighted magnetic resonance (MR) images. Iron-dependent contrast increases with magnetic field strength. We hypothesized that T2 mapping using 3 T MR imaging (MRI) can disclose differences between normal controls and AD subjects. Methods: High-resolution brain imaging protocols were developed and applied to 24 AD patients and 20 age-matched controls using 3 T MRI. Eight anatomical regions of interest were manually segmented, and T2 histograms were computed. A visual analysis technique, the heat map, was modified and applied to the large image data sets generated by these protocols. Results: A large number (163) of features from these histograms were examined, and 38 of these were significantly different (P < 0.05) between the groups. In the hippocampus, evidence was found for AD-related increases in iron deposition (shortened T2) and in the concentration of free tissue water (lengthened T2). Imaging of a section of postmortem brain before and after chemically extracting the iron established the presence of MRI-detectable iron in the hippocampus, cortex, and white matter in addition to brain regions traditionally viewed as containing high iron concentrations.

Differentiating human embryonic stem cells express a unique housekeeping gene signature.

Housekeeping genes (HKGs) are involved in basic functions needed for the sustenance of the cell and are assumed to be constitutively expressed at a constant level. Based on these features, HKGs are frequently used for normalization of gene expression data. In the present study, we used the CodeLink Gene Expression Bioarray system to interrogate changes in gene expression occurring during differentiation of human ESCs (hESCs). Notably, in the three hESC lines used for the study, we observed that the RNA levels of 56 frequently used HKGs varied to a degree that rendered them inappropriate as reference genes. Therefore, we defined a novel set of HKGs specifically for hESCs. Here we present a comprehensive list of 292 genes that are stably expressed (coefficient of variation <20%) in differentiating hESCs. These genes were further grouped into high‐, medium‐, and low‐expressed genes. The expression patterns of these novel HKGs show very little overlap with results obtained from somatic cells and tissues. We further explored the stability of this novel set of HKGs in independent, publicly available gene expression data from hESCs and observed substantial similarities with our results. Gene expression was confirmed by real‐time quantitative polymerase chain reaction analysis. Taken together, these results suggest that differentiating hESCs have a unique HKG signature and underscore the necessity to validate the expression profiles of putative HKGs. In addition, this novel set of HKGs can preferentially be used as controls in gene expression analyses of differentiating hESCs.

The relative distribution of membranous and cytoplasmic met is a prognostic indicator in stage I and II colon cancer.

Purpose: The association hepatocyte growth factor receptor (Met) tyrosine kinase with prognosis and survival in colon cancer is unclear, due in part to the limitation of detection methods used. In particular, conventional chromagenic immunohistochemistry (IHC) has several limitations including the inability to separate compartmental measurements. Measurement of membrane, cytoplasm, and nuclear levels of Met could offer a superior approach to traditional IHC. Experimental Design: Fluorescent-based IHC for Met was done in 583 colon cancer patients in a tissue microarray format. Using curvature and intensity-based image analysis, the membrane, nuclear, and cytoplasm were segmented. Probability distributions of Met within each compartment were determined, and an automated scoring algorithm was generated. An optimal score cutpoint was calculated using 500-fold crossvalidation of a training and test data set. For comparison with conventional IHC, a second array from the same tissue microarray block was 3,3′-diaminobenzidine immunostained for Met. Results: In crossvalidated and univariate Cox analysis, the membrane relative to cytoplasm Met score was a significant predictor of survival in stage I (hazard ratio, 0.16; P = 0.006) and in stage II patients (hazard ratio, 0.34; P ≤ 0.0005). Similar results were found with multivariate analysis. Met in the membrane alone was not a significant predictor of outcome in all patients or within stage. In the 3,3′-diaminobenzidine–stained array, no associations were found with Met expression and survival. Conclusions: These data indicate that the relative subcellular distribution of Met, as measured by novel automated image analysis, may be a valuable biomarker for estimating colon cancer prognosis.

Cardiomyogenic gene expression profiling of differentiating human embryonic stem cells

Human embryonic stem cells (hESCs) can differentiate into a variety of specialized cell types. Thus, they provide a model system for embryonic development to investigate the molecular processes of cell differentiation and lineage commitment. The development of the cardiac lineage is easily detected in mixed cultures by the appearance of spontaneously contracting areas of cells. We performed gene expression profiling of undifferentiated and differentiating hESCs and monitored 468 genes expressed during cardiac development and/or in cardiac tissue. Their transcription during early differentiation of hESCs through embryoid bodies (EBs) was investigated and compared with spontaneously differentiating hESCs maintained on feeders in culture without passaging (high-density (HD) protocol). We observed a larger variation in the gene expression between cells from a single cell line that were differentiated using two different protocols than in cells from different cell lines that were cultured according to the same protocol. Notably, the EB protocol resulted in more reproducible transcription profiles than the HD protocol. The results presented here provide new information about gene regulation during early differentiation of hESCs with emphasis on the cardiomyogenic program. In addition, we also identified regulatory elements that could prove critical for the development of the cardiomyocyte lineage.

Neural networks for longitudinal studies in Alzheimer's disease

OBJECTIVE Alzheimers disease affects a growing population of elderly people today. The predictions about the course of the disease is a key component of health care decision making for patients with Alzheimers. The physicians prognosis and predicted trajectory of cognitive decline often form the basis of treatment and health care decisions taken by patients and their families. These predictions are difficult to make because of the high variability and non-linearity exhibited by individual patterns of cognitive decline. This paper presents a new method of predicting the course of a disease using longitudinal data collected through multiple clinic visits. Longitudinal databases are similar to temporal databases, with some important differences--data is collected at irregular time intervals that are patient specific and also a varying number of observations are made for each patient, depending upon the number of times the patient visited the clinic. We propose a new type of neural network called the mixed effects neural network (MENN) model that can incorporate this type of longitudinal information. MATERIAL AND METHODS We have used longitudinal data on 704 subjects enrolled at the Layton aging and research center (LAARC) at Oregon Health and Science University. A back-propagation algorithm, modified for longitudinal data is used to obtain the weight parameters of the MENN. The modified back-propagation algorithm is further embedded in an iterative procedure that estimates the noise variance and the parameters that capture the longitudinal (temporal) correlation structure. RESULTS We have compared the performance of the MENN with linear mixed effects models and standard neural networks (NN). MENN show better performance (misclassification rate = 0.13 and relative MSE = 0.35) as compared to standard NN (misclassification rate = 0.34 and relative MSE = 2.74) and linear mixed effects models (misclassification rate = 0.14 and relative MSE = 0.4). CONCLUSION The results show that this method can be a useful tool for predicting non-linear disease trajectories and uncovering significant prognostic factors in longitudinal databases.

Predicting continuous epitopes in proteins

The ability to predict antigenic sites on proteins is crucial for the production of synthetic peptide vaccines and synthetic peptide probes of antibody structure. Large number of amino acid propensity scales based on various properties of the antigenic sites like hydrophilicity, flexibility/mobility, turns and bends have been proposed and tested previously. However these methods are not very accurate in predicting epitopes and non-epitope regions. We propose algorithms that combine 14 best performing individual propensity scales and give better prediction accuracy as compared to individual scales.