Molecular profiling of single cancer cells and clinical tissue specimens with semiconductor quantum dots

Semiconductor quantum dots (QDs) are a new class of fluorescent labels with broad applications in biomedical imaging, disease diagnostics, and molecular and cell biology. In comparison with organic dyes and fluorescent proteins, quantum dots have unique optical and electronic properties such as size-tunable light emission, improved signal brightness, resistance against photobleaching, and simultaneous excitation of multiple fluorescence colors. Recent advances have led to multifunctional nanoparticle probes that are highly bright and stable under complex in vitro and in vivo conditions. New designs involve encapsulating luminescent QDs with amphiphilic block copolymers, and linking the polymer coating to tumor-targeting ligands and drug-delivery functionalities. These improved QDs have opened new possibilities for real-time imaging and tracking of molecular targets in living cells, for multiplexed analysis of biomolecular markers in clinical tissue specimens, and for ultrasensitive imaging of malignant tumors in living animal models. In this article, we briefly discuss recent developments in bioaffinity QD probes and their applications in molecular profiling of individual cancer cells and clinical tissue specimens.     

Optimal molecular profiling of tissue and tissue components

Isolation of well-preserved pure cell populations is a prerequisite for sound studies of the molecular basis of any tissue-based biological phenomenon. This article reviews current methods for obtaining anatomically specific signals from molecules isolated from tissues, a basic requirement for productive linking of phenotype and genotype. The quality of samples isolated from tissue and used for molecular analysis is often glossed over or omitted from publications, making interpretation and replication of data difficult or impossible. Fortunately, recently developed techniques allow life scientists to better document and control the quality of samples used for a given assay, creating a foundation for improvement in this area. Tissue processing for molecular studies usually involves some or all of the following steps: tissue collection, gross dissection/identification, fixation, processing/embedding, storage/archiving, sectioning, staining, microdissection/annotation, and pure analyte labeling/identification and quantification. We provide a detailed comparison of some current tissue microdissection technologies, and provide detailed example protocols for tissue component handling upstream and downstream from microdissection. We also discuss some of the physical and chemical issues related to optimal tissue processing, and include methods specific to cytology specimens. We encourage each laboratory to use these as a starting point for optimization of their overall process of moving from collected tissue to high quality, appropriately anatomically tagged scientific results. In optimized protocols is a source of inefficiency in current life science research. Improvement in this area will significantly increase life science quality and productivity. The article is divided into introduction, materials, protocols, and notes sections. Because many protocols are covered in each of these sections, information relating to a single protocol is not contiguous. To get the greatest benefit from this article, readers are advised to read through the entire article first, identify protocols appropriate to their laboratory for each step in their workflow, and then reread entries in each section pertaining to each of these single protocols.     

Antibody-based tissue profiling as a tool for clinical proteomics

Here, we show a strategy for high-throughput antibody-based tissue profiling with the aim to create an atlas of protein expression patterns in normal human tissues and cancer tissues representing the 20 most prevalent cancer types. A set of standardized tissue microarrays (TMAs) was produced to allow for rapid screening of a multitude of different cells and tissues using immunohistochemistry. Eight TMA blocks were produced containing 48 different normal human tissues in triplicate and cancer tissue from 216 individually different tumors in duplicate. Sections from these blocks were immunohistochemically stained using five commercial and five in-house generated antibodies. Digital images for annotation of expression profiles were generated using a semiautomated approach. Five hundred seventy-six images and annotation data corresponding to a total of 30 Gbytes of data were collected for each antibody. The data presented here suggest that antibody-based profiling of protein expression in tissues can be used as a valuable tool in clinical proteomics.     

Molecular profiling of single cells and tissue specimens with quantum dots

Quantum dots are tiny light-emitting particles on the nanometer scale. They are emerging as a new class of biological label with properties and applications that are not available with traditional organic dyes and fluorescent proteins. Recent advances, as reported in Science and Nature Biotechnology, have led to quantum dot bioconjugates that are highly luminescent and stable. These bioconjugates raise new possibilities for studying genes, proteins and drug targets in single cells, tissue specimens and even in living animals.     

Signal pathway profiling of ovarian cancer from human tissue specimens using reverse-phase protein microarrays

Defects in cell signaling pathways play a central role in cancer cell growth, survival, invasion and metastasis. An important goal of proteomics is to characterize and develop "circuit maps" of these signaling pathways in normal and diseased cells. We have used reverse-phase protein array technology coupled with laser capture microdissection and phospho-specific antibodies to examine the activation status of several key molecular "gates" involved in cell survival and proliferation signaling in human ovarian tumor tissue. The levels of activated extracellular-regulated kinase (ERK1/2) varied considerably in tumors of the same histotype, but no significant differences between histotypes were observed. Advanced stage tumors had slightly higher levels of phosphorylated ERK1/2 compared to early stage tumors. The activation status of Akt and glycogen synthase kinase 3beta, key proteins and indicators of the state of the phosphatidylinositol 3-kinase/Akt pro-survival pathway also showed more variation within each histotype than between the histotypes studied. Our results demonstrate the utility of reverse phase protein microarrays for the multiplexed analysis of signal transduction from discreet cell populations of cells procured directly from human ovarian tumor specimens and suggest that patterns in signal pathway activation in ovarian tumors may be patient-specific rather than type or stage specific.     

A reliable fluorescent stain for fungi in tissue sections and clinical specimens

A simple and reliable staining technique is described using the fluorescent brightener Blankophor BA which binds specifically to fungal cell wall components. Potential diagnostic applications are shown.     

Semisupervised learning for molecular profiling

Class prediction and feature selection are two learning tasks that are strictly paired in the search of molecular profiles from microarray data. Researchers have become aware how easy it is to incur a selection bias effect, and complex validation setups are required to avoid overly optimistic estimates of the predictive accuracy of the models and incorrect gene selections. This paper describes a semisupervised pattern discovery approach that uses the by-products of complete validation studies on experimental setups for gene profiling. In particular, we introduce the study of the patterns of single sample responses (sample-tracking profiles) to the gene selection process induced by typical supervised learning tasks in microarray studies. We originate sample-tracking profiles as the aggregated off-training evaluation of SVM models of increasing gene panel sizes. Genes are ranked by E-RFE, an entropy-based variant of the recursive feature elimination for support vector machines (RFE-SVM). A dynamic time warping (DTW) algorithm is then applied to define a metric between sample-tracking profiles. An unsupervised clustering based on the DTW metric allows automating the discovery of outliers and of subtypes of different molecular profiles. Applications are described on synthetic data and in two gene expression studies.     

Sample size calculations for designing clinical proteomic profiling studies using mass spectrometry

In cancer clinical proteomics, MALDI and SELDI profiling are used to search for biomarkers of potentially curable early-stage disease. A given number of samples must be analysed in order to detect clinically relevant differences between cancers and controls, with adequate statistical power. From clinical proteomic profiling studies, expression data for each peak (protein or peptide) from two or more clinically defined groups of subjects are typically available. Typically, both exposure and confounder information on each subject are also available, and usually the samples are not from randomized subjects. Moreover, the data is usually available in replicate. At the design stage, however, covariates are not typically available and are often ignored in sample size calculations. This leads to the use of insufficient numbers of samples and reduced power when there are imbalances in the numbers of subjects between different phenotypic groups. A method is proposed for accommodating information on covariates, data imbalances and design-characteristics, such as the technical replication and the observational nature of these studies, in sample size calculations. It assumes knowledge of a joint distribution for the protein expression values and the covariates. When discretized covariates are considered, the effect of the covariates enters the calculations as a function of the proportions of subjects with specific attributes. This makes it relatively straightforward (even when pilot data on subject covariates is unavailable) to specify and to adjust for the effect of the expected heterogeneities. The new method suggests certain experimental designs which lead to the use of a smaller number of samples when planning a study. Analysis of data from the proteomic profiling of colorectal cancer reveals that fewer samples are needed when a study is balanced than when it is unbalanced, and when the IMAC30 chip-type is used. The method is implemented in the clippda package and is available in R at: http://www.bioconductor.org/help/bioc-views/release/bioc/html/clippda.html.     

Improved RT-PCR amplification for molecular analyses with long-term preserved formalin-fixed, paraffin-embedded tissue specimens.

Recently, in addition to DNA, RNA extracted from archival tissue specimens has become an invaluable source of material for molecular biological analysis. Successful amplification with PCR/RT-PCR is problematic when using amplicons of short size due to degradation of DNA or RNA. We established an improved method for efficient RT-PCR amplification of RNA extracted from archival formalin-fixed, paraffin-embedded tissue by the elimination of RNA modification and the restoration of RNA template activity. Namely, the preheating in citrate buffer (pH 4.0) of RNA extracted from long-term preserved tissue specimens resulted in significantly increased efficiency of RT-PCR.     

Saturation labeling with cysteine-reactive cyanine fluorescent dyes provides increased sensitivity for protein expression profiling of laser-microdissected clinical specimens

Laser capture microdissection (LCM) provides the capability to isolate and analyze small numbers of cells from a specific area of a histologic section. LCM has particular value for analysis of early stage tumors, which are often small and intermixed with non-tumor tissue. It has previously been shown that a new generation of cysteine-reactive cyanine dyes can, in principle, provide increased sensitivity for two-dimensional fluorescence difference gel electrophoresis (2-D DIGE) profiling when sample quantitities are limiting. However, the comparative advantage of the new dyes in a clinical setting has not been established. Here, we report that cysteine-reactive dyes allowed the identification of more features than established, lysine-reactive dyes with a given number of cells. This was true both with extracts prepared from human papillomavirus E6 and E7-transduced human keratinocytes, a model for early-stage cervical cancer, and with LCM samples. In an experiment comparing LCM clinical samples of gastric adenocarcinoma versus precancerous, spasmolytic polypeptide expressing metaplasia (SPEM) from the same patient, cysteine labeling allowed the identification of more than 1000 discrete protein spots in samples containing 5000 cells. This is a 5- to 50-fold smaller sample than used in previous studies. Both labeling methods had a comparable success rate for protein identification by mass spectrometry (MS). The proteins associated with more than 40 differentially abundant spots in the clinical samples were identified by MS. In this exploratory analysis, changes in expression levels of cytoskeletal proteins, molecular chaperones, and cell-signaling proteins were seen. The identification of a number of proteins that are potentially relevant to tumor progression suggests that the method holds promise for biomarker discovery.     

Towards a rapid molecular diagnostic for melioidosis: Comparison of DNA extraction methods from clinical specimens

Optimising DNA extraction from clinical samples for Burkholderia pseudomallei Type III secretion system real-time PCR in suspected melioidosis patients confirmed that urine and sputum are useful diagnostic samples. Direct testing on blood remains problematic; testing DNA extracted from plasma was superior to DNA from whole blood or buffy coat.     

Imaging of atomic and molecular species in tissue with Laser-SNMS for pharmaceutical studies

The success of several anti-cancer therapies as well as other therapeutic and diagnostic strategies relies on the ability to selectively deliver compounds to target cells while sparing normal tissue. For many applications, however, current analytical methods lack the sensitivity and selectivity necessary to determine the distribution of pharmaceutical ultra-trace compounds within tissues with sub-cellular resolution. Laser secondary neutral mass spectrometry (Laser-SNMS) and time-of-flight secondary ion mass spectrometry (TOF-SIMS) are capable of detecting atoms and molecules with high sensitivity and a spatial resolution of up to 100 nm. The use of such methods requires special preparation techniques which preserve the morphological and chemical integrity of the living cell. Laser-SNMS was used to verify the effectiveness of the delivery process for various pharmaceutical compounds in animal studies. After injection of the pharmaceuticals, different types of mouse tissue such as brain, kidney and tumors were extracted, then prepared on a special specimen carrier and subsequently plunged with high velocity into LN₂-cooled propane for cryofixation. After trimming, the tissue block was freeze-dried. For postionization of sputtered neutrals, a laser beam with a wavelength of 193 nm was used. Ion-induced electron images showed that the structural and chemical integrity of the cells had been preserved. Cell-specific elemental and molecular signals could be used to identify individual cells and cell nuclei. The obtained data yield information about the distribution of the pharmaceutical products in different kinds of tissue.     

Biochemical and molecular studies using human autopsy brain tissue

The use of human brain tissue obtained at autopsy for neurochemical, pharmacological and physiological analyses is reviewed. RNA and protein samples have been found suitable for expression profiling by techniques that include RT-PCR, cDNA microarrays, western blotting, immunohistochemistry and proteomics. The rapid development of molecular biological techniques has increased the impetus for this work to be applied to studies of brain disease. It has been shown that most nucleic acids and proteins are reasonably stable post-mortem. However, their abundance and integrity can exhibit marked intra- and intercase variability, making comparisons between case-groups difficult. Variability can reveal important functional and biochemical information. The correct interpretation of neurochemical data must take into account such factors as age, gender, ethnicity, medicative history, immediate ante-mortem status, agonal state and post-mortem and post-autopsy intervals. Here we consider issues associated with the sampling of DNA, RNA and proteins using human autopsy brain tissue in relation to various ante- and post-mortem factors. We conclude that valid and practical measures of a variety of parameters may be made in human brain tissue, provided that specific factors are controlled.     

Chemometrics models for overcoming high between subject variability: applications in clinical metabolic profiling studies

In human metabolic profiling studies, between-subject variability is often the dominant feature and can mask the potential classifications of clinical interest. Conventional models such as principal component analysis (PCA) are usually not effective in such situations and it is therefore highly desirable to find a suitable model which is able to discover the underlying pattern hidden behind the high between-subject variability. In this study we employed two clinical metabolomics data sets as the testing grounds, in which such variability had been observed, and we demonstrate that a proper choice of chemometrics model can help to overcome this issue of high between-subject variability. Two data sets were used to represent two different types of experiment designs. The first data set was obtained from a small-scale study investigating volatile organic compounds (VOCs) collected from chronic wounds using a skin patch device and analysed by thermal desorption-gas chromatography-mass spectrometry. Five patients were recruited and for each patient three sites sampled in triplicate: healthy skin, boundary of the lesion and top of the lesion, the aim was to discriminate these three types of samples based on their VOC profile. The second data set was from a much larger study involving 35 healthy subjects, 47 patients with chronic obstructive pulmonary disease and 33 with asthma. The VOCs in the breath of each subject were collected using a mask device and analysed again by GC–MS with the aim of discriminating the three types of subjects based on breath VOC profiles. Multilevel simultaneous component analysis, multilevel partial least squares for discriminant analysis, ANOVA-PCA, and a novel simplified ANOVA-PCA model—which we have named ANOVA-Mean Centre (ANOVA-MC)—were applied on these two data sets. Significantly improved results were obtained by using these models. We also present a novel validation procedure to verify statistically the results obtained from those models.