Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing

The current prognosis and classification of CRC relies on staging systems that integrate histopathologic and clinical findings. However, in the majority of CRC cases, cell dysfunction is the result of numerous mutations that modify protein expression and post-translational modification¹.A number of cell surface antigens, including cluster of differentiation (CD) antigens, have been identified as potential prognostic or metastatic biomarkers in CRC. These antigens make ideal biomarkers as their expression often changes with tumour progression or interactions with other cell types, such as tumour-infiltrating lymphocytes (TILs) and tumour-associated macrophages (TAMs).The use of immunohistochemistry (IHC) for cancer sub-classification and prognostication is well established for some tumour types^2,3. However, no single ‘marker’ has shown prognostic significance greater than clinico-pathological staging or gained wide acceptance for use in routine pathology reporting of all CRC cases. A more recent approach to prognostic stratification of disease phenotypes relies on surface protein profiles using multiple ''markers''. While expression profiling of tumours using proteomic techniques such as iTRAQ is a powerful tool for the discovery of biomarkers4, it is not optimal for routine use in diagnostic laboratories and cannot distinguish different cell types in a mixed population. In addition, large amounts of tumour tissue are required for the profiling of purified plasma membrane glycoproteins by these methods. In this video we described a simple method for surface proteome profiling of viable cells from disaggregated CRC samples using a DotScan CRC antibody microarray. The 122-antibody microarray consists of a standard 82-antibody region recognizing a range of lineage-specific leukocyte markers, adhesion molecules, receptors and markers of inflammation and immune response⁵, together with a satellite region for detection of 40 potentially prognostic markers for CRC. Cells are captured only on antibodies for which they express the corresponding antigen. The cell density per dot, determined by optical scanning, reflects the proportion of cells expressing that antigen, the level of expression of the antigen and affinity of the antibody⁶. For CRC tissue or normal intestinal mucosa, optical scans reflect the immunophenotype of mixed populations of cells. Fluorescence multiplexing can then be used to profile selected sub-populations of cells of interest captured on the array. For example, Alexa 647-anti-epithelial cell adhesion molecule (EpCAM; CD326), is a pan-epithelial differentiation antigen that was used to detect CRC cells and also epithelial cells of normal intestinal mucosa, while Phycoerythrin-anti-CD3, was used to detect infiltrating T-cells⁷. The DotScan CRC microarray should be the prototype for a diagnostic alternative to the anatomically-based CRC staging system.     

病原宏基因组高通量测序技术质量控制与评价的挑战和思考

病原宏基因组高通量测序技术理论上能够检测几乎所有病原体基因组核酸,且适用于几乎所有类型的临床样本,尤其适用于病原不明的疑难感染性疾病的诊断。因此该技术正逐渐成为实验室常规检测方法的重要补充和不可替代的项目。然而,基于该技术的诊断试剂不仅检测流程繁琐复杂、技术更新迭代速度较快,同时相关质量控制与评价的方法和标准也有待明确,这些因素均给该技术的临床转化应用、行业发展以及监管带来挑战和不确定性。文中简述了该技术的原理和优势,以及检测流程和关键质量控制环节,最后着重介绍了关于该技术的质量评价方法和标准的相关思考。     

Biomarker discovery in microarray gene expression data with Gaussian processes

MOTIVATION: In clinical practice, pathological phenotypes are often labelled with ordinal scales rather than binary, e.g. the Gleason grading system for tumour cell differentiation. However, in the literature of microarray analysis, these ordinal labels have been rarely treated in a principled way. This paper describes a gene selection algorithm based on Gaussian processes to discover consistent gene expression patterns associated with ordinal clinical phenotypes. The technique of automatic relevance determination is applied to represent the significance level of the genes in a Bayesian inference framework. RESULTS: The usefulness of the proposed algorithm for ordinal labels is demonstrated by the gene expression signature associated with the Gleason score for prostate cancer data. Our results demonstrate how multi-gene markers that may be initially developed with a diagnostic or prognostic application in mind are also useful as an investigative tool to reveal associations between specific molecular and cellular events and features of tumour physiology. Our algorithm can also be applied to microarray data with binary labels with results comparable to other methods in the literature.     

Molecular markers of circulating melanoma cells

Of all skin cancers, cutaneous malignant melanoma (CMM) is the most aggressive and the life expectancy of patients with lymphatic or systemic metastases is dramatically reduced. Understandably therefore, scientists and clinicians have focused on improving diagnostic and prognostic techniques. Of these, perhaps the most promising are multimarker real-time RT-PCR and microarray for detection of circulating CMM cells in peripheral blood. While the optimal set of markers is still to be identified that can accurately assess disease severity and progression at all clinical stages of the disease, recent progress has been dramatic. Here we provide an exhaustive review of recent studies in which a variety of markers are assessed. Moreover, the efficacy of the markers relative to clinical stage is discussed in light of experimental findings. From these studies, it is apparent that researchers are now much closer to defining a set of markers of circulating cells that can be utilized in routine diagnostic tests.     

Role of gene expression profiling for diagnosing acute leukemias

Cytomorphology and cytochemistry in combination with multiparameter immunophenotyping today are the standard methods for establishing the diagnosis of acute leukemias. In addition, cytogenetics, fluorescence in situ hybridization, and polymerase chain reaction based assays provide important information regarding biologically defined and prognostically relevant subgroups and allow a comprehensive diagnosis of well defined subentities. With regard to the clinical setting a better understanding of the clinical course of distinct biologically defined disease subtypes is needed to select disease-specific therapeutic approaches. Paralleling the increase in knowledge on deregulated pathways in leukemia the development of new therapeutics is accelerated and therefore requires a detailed and comprehensive diagnostic tool. Revealing and quantifying the expression status of many ten thousands of genes in a single analysis the microarray technology holds this potential to become an essential tool for the molecular classification of leukemias. It may therefore be used as a routine method for diagnostic purposes in the near future. Furthermore, it is anticipated that new biologically defined and clinically relevant subtypes of leukemia will be identified based on gene expression profiling. This method may therefore guide therapeutic decisions.     

The principles and advances of quantitative pathology

This article gives an overview of the quantitative pathologic techniques used today, with special emphasis on interactive morphometry and its application in the clinical setting. At present, stereologic calculations may be necessary, but in a diagnostic setting have only rarely proved to be essential. The reproducibility of the measurements is discussed in relation to the definition of the particles and staining methods. A number of technical factors that cause random errors are mentioned, such as quality of the slides, magnification, definition of the particles to be measured and measuring protocol. Large-scale experiments have revealed that the means of nuclear morphometric and certain stereologic features are reproducible, such as in the volume percentage of epithelium, the surface densities of glands and the mitotic activity index. The diagnostic applications of the standard deviation and shape factors of nuclear quantitative features require additional precautions, however. Having quantified cell and tissue features, multivariate analysis may result in a better discrimination of two or more groups under study. The quantitative pathologic examination of cells and tissues can provide important diagnostic and prognostic information. Quantitation in pathology is especially useful in so-called continuous lesions, in which interobserver and intraobserver disagreement is considerable. An important requirement of diagnostic morphometry is object selection by a skilled pathologist; the use of morphometry as a black box can result in dramatic errors. The criteria used for a morphometric classification rule that can be used for clinical applications are summarized. Quality control of the whole measuring system is essential. Application of these techniques for more than six years in diagnostic pathology has repeatedly corrected previous qualitative diagnoses.(ABSTRACT TRUNCATED AT 250 WORDS)     

Comparison of five methods for determination of total plasma protein concentration

Quantitation of exact total protein content is often a key step and is common to many applications in general biochemistry research and routine clinical laboratory practice. Before embarking on any type of protein analysis, particularly comparative techniques, it is important to accurately quantitate the amount of protein in the sample. In order to assess the quality of total protein estimation results, five methods were tested and were applied to the same pooled plasma sample. For this aim, Bradford (Coomassie Brilliant Blue), Lowry (Folin-Ciocalteau), Biüret, Pesce and Strande (Ponceau-S/TCA), and modified method of Schaffner-Weismann (Amido Black 10B) were used. The last two methods employ simultaneous precipitation of proteins with the acid containing dye solutions followed by dissolution of precipitate in a NaOH solution. It is shown that each assay has advantages and disadvantages relative to sensitivity, ease of performance, acceptance in literature, accuracy and reproducibility/coefficient of variation. All of the methods tested show a CV % < 6. Besides pooled plasma, a known concentration of human serum albumin was also analyzed and discussed by means of standardization of plasma total protein content.     

DNA fragmentation simulation method (FSM) and fragment size matching improve aCGH performance of FFPE tissues

Whole-genome copy number analysis platforms, such as array comparative genomic hybridization (aCGH) and single nucleotide polymorphism (SNP) arrays, are transformative research discovery tools. In cancer, the identification of genomic aberrations with these approaches has generated important diagnostic and prognostic markers, and critical therapeutic targets. While robust for basic research studies, reliable whole-genome copy number analysis has been unsuccessful in routine clinical practice due to a number of technical limitations. Most important, aCGH results have been suboptimal because of the poor integrity of DNA derived from formalin-fixed paraffin-embedded (FFPE) tissues. Using self-hybridizations of a single DNA sample we observed that aCGH performance is significantly improved by accurate DNA size determination and the matching of test and reference DNA samples so that both possess similar fragment sizes. Based on this observation, we developed a novel DNA fragmentation simulation method (FSM) that allows customized tailoring of the fragment sizes of test and reference samples, thereby lowering array failure rates. To validate our methods, we combined FSM with Universal Linkage System (ULS) labeling to study a cohort of 200 tumor samples using Agilent 1 M feature arrays. Results from FFPE samples were equivalent to results from fresh samples and those available through the glioblastoma Cancer Genome Atlas (TCGA). This study demonstrates that rigorous control of DNA fragment size improves aCGH performance. This methodological advance will permit the routine analysis of FFPE tumor samples for clinical trials and in daily clinical practice.     

Comparison of five methods for determination of total plasma protein concentration

Quantitation of exact total protein content is often a key step and is common to many applications in general biochemistry research and routine clinical laboratory practice. Before embarking on any type of protein analysis, particularly comparative techniques, it is important to accurately quantitate the amount of protein in the sample. In order to assess the quality of total protein estimation results, five methods were tested and were applied to the same pooled plasma sample. For this aim, Bradford (Coomassie Brilliant Blue), Lowry (Folin–Ciocalteau), Biüret, Pesce and Strande (Ponceau–S/TCA), and modified method of Schaffner–Weismann (Amido Black 10B) were used. The last two methods employ simultaneous precipitation of proteins with the acid containing dye solutions followed by dissolution of precipitate in a NaOH solution. It is shown that each assay has advantages and disadvantages relative to sensitivity, ease of performance, acceptance in literature, accuracy and reproducibility/coefficient of variation. All of the methods tested show a CV % < 6. Besides pooled plasma, a known concentration of human serum albumin was also analyzed and discussed by means of standardization of plasma total protein content.     

Effect of various normalization methods on Applied Biosystems expression array system data

Background  

DNA microarray technology provides a powerful tool for characterizing gene expression on a genome scale. While the technology has been widely used in discovery-based medical and basic biological research, its direct application in clinical practice and regulatory decision-making has been questioned. A few key issues, including the reproducibility, reliability, compatibility and standardization of microarray analysis and results, must be critically addressed before any routine usage of microarrays in clinical laboratory and regulated areas can occur. In this study we investigate some of these issues for the Applied Biosystems Human Genome Survey Microarrays.     

The cytopathologist's role in developing and evaluating artificial intelligence in cytopathology practice

Artificial intelligence (AI) technologies have the potential to transform cytopathology practice, and it is important for cytopathologists to embrace this and place themselves at the forefront of implementing these technologies in cytopathology. This review illustrates an archetypal AI workflow from project conception to implementation in a diagnostic setting and illustrates the cytopathologist's role and level of involvement at each stage of the process. Cytopathologists need to develop and maintain a basic understanding of AI, drive decisions regarding the development and implementation of AI in cytopathology, participate in the generation of datasets used to train and evaluate AI algorithms, understand how the performance of these algorithms is assessed, participate in the validation of these algorithms (either at a regulatory level or in the laboratory setting), and ensure continuous quality assurance of algorithms deployed in a diagnostic setting. In addition, cytopathologists should ensure that these algorithms are developed, trained, tested and deployed in an ethical manner. Cytopathologists need to become informed consumers of these AI algorithms by understanding their workings and limitations, how their performance is assessed and how to validate and verify their output in clinical practice.     

The value of microarray techniques for quantitative gene profiling in molecular diagnostics

There has been an explosion of interest in microarray technologies that allow the quantification of whole-genome RNA expression data. The apparent correlation of expression profiles with clinically relevant parameters such as disease outcome has raised expectations with respect to the clinical usefulness of the data generated. Yet the accuracy and biological relevance of these data remain contentious, even in basic research applications. Therefore, numerous issues related to format, quality, validation and interpretation remain to be resolved before microarray profiling can become a diagnostic tool of clinical relevance for routine work.     

Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study

Although prognostic gene expression signatures for survival in early-stage lung cancer have been proposed, for clinical application, it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) could be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early-stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas.     

Estimation and control of multiple testing error rates for microarray studies

The analysis of microarray data often involves performing a large number of statistical tests, usually at least one test per queried gene. Each test has a certain probability of reaching an incorrect inference; therefore, it is crucial to estimate or control error rates that measure the occurrence of erroneous conclusions in reporting and interpreting the results of a microarray study. In recent years, many innovative statistical methods have been developed to estimate or control various error rates for microarray studies. Researchers need guidance choosing the appropriate statistical methods for analysing these types of data sets. This review describes a family of methods that use a set of P-values to estimate or control the false discovery rate and similar error rates. Finally, these methods are classified in a manner that suggests the appropriate method for specific applications and diagnostic procedures that can identify problems in the analysis are described.     

Application of stains in clinical microbiology

Stains have been used for diagnosing infectious diseases since the late 1800s. The Gram stain remains the most commonly used stain because it detects and differentiates a wide range of pathogens. The next most commonly used diagnostic technique is acid-fast staining that is used primarily to detect Mycobacterium tuberculosis and other severe infections. Many infectious agents grow slowly on culture media or may not grow at all; stains may be the only method to detect these organisms in clinical specimens. In the hands of experienced clinical microscopists, stains provide rapid and cost-effective information for preliminary diagnosis of infectious diseases. A review of the most common staining methods used in the clinical microbiology laboratory is presented here.     

SNP array analysis in constitutional and cancer genome diagnostics--copy number variants, genotyping and quality control

Array-based comparative genomic hybridization analysis of genomic DNA was first applied in postnatal diagnosis for patients with intellectual disability (ID) and/or congenital anomalies (CA). Genome-wide single-nucleotide polymorphism (SNP) array analysis was subsequently implemented as the first line diagnostic test for ID/CA patients in our laboratory in 2009, because its diagnostic yield is significantly higher than that of routine cytogenetic analysis. In addition to the detection of copy number variations, the genotype information obtained with SNP array analysis enables the detection of stretches of homozygosity and thereby the possible identification of recessive disease genes, mosaic aneuploidy, or uniparental disomy. Patient-parent (trio) information analysis is used to screen for the presence of any form of uniparental disomy in the patient and can determine the parental origin of a de novo copy number variation. Moreover, the outcome of a genotype analysis is used as a final quality control by ruling out potential sample mismatches due to non-paternity or sample mix-up. SNP array analysis is now also used in our laboratory for patients with disorders for which locus heterogeneity is known (homozygosity pre-screening), in prenatal diagnosis in case of structural ultrasound anomalies, and for patients with leukemia. In this report, we summarize our array findings and experiences in the various diagnostic applications and demonstrate the power of a SNP-based array platform for molecular karyotyping, because it not only significantly improves the diagnostic yield in both constitutional and cancer genome diagnostics, but it also enhances the quality of the diagnostic laboratory workflow.     

Application of stains in clinical microbiology

Stains have been used for diagnosing infectious diseases since the late 1800s. The Gram stain remains the most commonly used stain because it detects and differentiates a wide range of pathogens. The next most commonly used diagnostic technique is acid-fast staining that is used primarily to detect Mycobacterium tuberculosis and other severe infections. Many infectious agents grow slowly on culture media or may not grow at all; stains may be the only method to detect these organisms in clinical specimens. In the hands of experienced clinical microscopists, stains provide rapid and cost-effective information for preliminary diagnosis of infectious diseases. A review of the most common staining methods used in the clinical microbiology laboratory is presented here.     

Should Seminal Oxidative Stress Measurement be Offered Routinely to Men Presenting for Infertility Evaluation?

ObjectiveTo determine if seminal oxidative stress measurement should be offered routinely to men presenting for infertility evaluation.MethodsWe performed an extensive review of the English-language literature by searching MEDLINE for studies published between 1980 and 2007.ResultsResearch conducted during the last decade has provided growing support for the concept that excessive production of reactive oxygen species (ROS) is related to abnormal semen parameters and sperm damage. Routine semen analysis remains the backbone of clinical evaluation in male infertility, but determining the levels and sources of excessive ROS generation in semen is currently not included in the routine evaluation of subfertile men. However, the diagnostic and prognostic capabilities of seminal oxidative stress measurement exceed the capabilities of conventional sperm quality tests. An oxidative stress test may accurately discriminate between fertile and infertile men and identify those with a clinical diagnosis of male factor infertility who are likely to initiate a pregnancy if they are followed over a period of time. In addition, such a test can help select subgroups of patients with infertility in which oxidative stress is an important factor and those who may benefit from antioxidant supplementation. Although consensus is still required about the type and dosage of antioxidants to be used, rationale and evidence exist supporting their use in infertile men with elevated oxidative stress.ConclusionConsensus is growing about the clinical utility of seminal oxidative stress testing in infertility clinics, but standardization of protocols to measure ROS is crucial before introducing these tests into routine clinical practice. (Endocr Pract. 2008;14:484-491)     

Conventional methods and future trends in antimicrobial susceptibility testing

Antimicrobial susceptibility testing is an essential task for selecting appropriate antimicrobial agents to treat infectious diseases. Constant evolution has been observed in methods used in the diagnostic microbiology laboratories. Disc diffusion or broth microdilution are classical and conventional phenotypic methods with long turnaround time and labour-intensive but still widely practiced as gold-standard. Scientists are striving to develop innovative, novel and faster methods of antimicrobial susceptibility testing to be applicable for routine microbiological laboratory practice and research. To meet the requirements, there is an increasing trend towards automation, genotypic and micro/nano technology-based innovations. Automation in detection systems and integration of computers for online data analysis and data sharing are giant leaps towards versatile nature of automated methods currently in use. Genotypic methods detect a specific genetic marker associated with resistant phenotypes using molecular amplification techniques and genome sequencing. Microfluidics and microdroplets are recent addition in the continuous advancement of methods that show great promises with regards to safety and speed and have the prospect to identify and monitor resistance mechanisms. Although genotypic and microfluidics methods have many exciting features, however, their applications into routine clinical laboratory practice warrant extensive validation. The main impetus behind the evolution of methods in antimicrobial susceptibility testing is to shorten the overall turnaround time in obtaining the results and to enhance the ease of sample processing. This comprehensive narrative review summarises major conventional phenotypic methods and automated systems currently in use, and highlights principles of some of the emerging genotypic and micro/nanotechnology-based methods in antimicrobial susceptibility testing.