G. Valet

Approaching clinical proteomics: current state and future fields of application in fluid proteomics

Recent developments in proteomics technology offer new opportunities for clinical applications in hospital or specialized laboratories including the identification of novel biomarkers, monitoring of disease, detecting adverse effects of drugs, and environmental hazards. Advanced spectrometry technologies and the development of new protein array formats have brought these analyses to a standard, which now has the potential to be used in clinical diagnostics. Besides standardization of methodologies and distribution of proteomic data into public databases, the nature of the human body fluid proteome with its high dynamic range in protein concentrations, its quantitation problems, and its extreme complexity present enormous challenges. Molecular cell biology (cytomics) with its link to proteomics is a new fast moving scientific field, which addresses functional cell analysis and bioinformatic approaches to search for novel cellular proteomic biomarkers or their release products into body fluids that provide better insight into the enormous biocomplexity of disease processes and are suitable for patient stratification, therapeutic monitoring, and prediction of prognosis. Experience from studies of in vitro diagnostics and especially in clinical chemistry showed that the majority of errors occurs in the preanalytical phase and the setup of the diagnostic strategy. This is also true for clinical proteomics where similar preanalytical variables such as inter‐ and intra‐assay variability due to biological variations or proteolytical activities in the sample will most likely also influence the results of proteomics studies. However, before complex proteomic analysis can be introduced at a broader level into the clinic, standardization of the preanalytical phase including patient preparation, sample collection, sample preparation, sample storage, measurement, and data analysis is another issue which has to be improved. In this report, we discuss the recent advances and applications that fulfill the criteria for clinical proteomics with the focus on cellular proteomics (cytoproteomics) as related to preanalytical and analytical standardization and to quality control measures required for effective implementation of these technologies and analytes into routine laboratory testing to generate novel actionable health information. It will then be crucial to design and carry out clinical studies that can eventually identify novel clinical diagnostic strategies based on these techniques and validate their impact on clinical decision making.

Cytomics—New Technologies: Towards a Human Cytome Project

Molecular cell systems research (cytomics) aims at the understanding of the molecular architecture and functionality of cell systems (cytomes) by single‐cell analysis in combination with exhaustive bioinformatic knowledge extraction. In this way, loss of information as a consequence of molecular averaging by cell or tissue homogenisation is avoided.

Relevance of the chemical charge of rhodamine dyes to multiple drug resistance

Previously, we have shown that multiple drug resistant (MDR) Friend leukemia cells (FLC) are cross-resistant to the positively-charged dye, Rhodamine 123 (Rho 123), and that this resistance can be reversed by verapamil (VER). In the present study we used two zwitterionic rhodamine analogs, Rhodamine 116 and Rhodamine 110, and another positively-charged analog, Rhodamine 6G, to determine whether drug accumulation, resistance and modulation were affected by changes in the charge of these compounds. While there was no differential sensitivity between sensitive and resistant FLC to zwitterionic rhodamines, there was marked differential toxicity between these cell types for the positively-charged analogs. The IC50 values were 1000- and 100-fold greater in resistant than in sensitive cells for Rho 123 and Rho 6G respectively. Intracellular drug accumulation was significantly higher in sensitive as compared to resistant cells for both Rho 123 and Rho 6G, but little difference in drug uptake between these two cell types was observed for Rho 110 and Rho 116. It was also found that the intracellular to extracellular ratio of the positively-charged compounds was greater than unity in both sensitive and resistant cells whereas for the zwitterionic analogs this ratio was less than 1. Furthermore, this ratio of drug uptake was found to be significantly higher for Rho 6G than for Rho 123, which correlated with the high oil:water partition coefficient of Rho 6G (115.6). In MDR cells, verapamil increased Rho 123 and Rho 6G accumulation by 9.4- and 8.6-fold respectively. In addition, IC50 values in resistant cells were reduced greater than 100-fold for Rho 6G and greater than 1000-fold for Rho 123 in the presence of 10 micrograms/ml of verapamil. In contrast, less than 2-fold reduction of IC50 values for both of the zwitterionic analogs could be obtained under the same conditions. These results indicate that the chemical charge of rhodamines plays an important role in their differential accumulation, cytotoxicity and sensitivity to modulators such as verapamil, in sensitive and multi-drug resistant cells. The data also suggest that increased lipophilicity of the positively-charged rhodamines may increase their ability to accumulate in, and subsequently kill, MDR cells.

Automated classification of patients with chronic lymphocytic leukemia and immunocytoma from flow cytometric three‐color immunophenotypes

The goal of this study was the discrimination between chronic lymphocytic leukemia (B-CLL), clinically more aggressive lymphoplasmocytoid immunocytoma (LP-IC) and other low-grade non-Hodgkins lymphomas (NHL) of the B-cell type by automated analysis of flow cytometric immunophenotypes CD45/14/20, CD4/8/3, kappa/CD19/5, lambda/CD19/5 and CD10/23/19 from peripheral blood and bone marrow aspirate leukocytes using the multiparameter classification program CLASSIF1. The immunophenotype list mode files were exhaustively evaluated by combined lymphocyte, monocyte, and granulocyte (LMG) analysis. The results were introduced into databases and automatically classified in a standardized way. The resulting triple matrix classifiers are laboratory and instrument independent, error tolerant, and robust in the classification of unknown test samples. Practically 100% correct individual patient classification was achievable, and most manually unclassifiable patients were unambiguously classified. It is of interest that the single lambda/CD19/5 antibody triplet provided practically the same information as the full set of the five antibody triplets. This demonstrates that standardized classification can be used to optimize immunophenotype panels. On-line classification of test samples is accessible on the Internet: http://www.biochem.mpg.de/valet/leukaem1.html Immunophenotype panels are usually devised for the detection of the frequency of abnormal cell populations. As shown by computer classification, most the highly discriminant information is, however, not contained in percentage frequency values of cell populations, but rather in total antibody binding, antibody binding ratios, and relative antibody surface density parameters of various lymphocyte, monocyte, and granulocyte cell populations.

Lymphoma discrimination by computerized triple matrix analysis of list mode data from three-color flow cytometric immunophenotypes of bone marrow aspirates.

BACKGROUND The goal of this study was to evaluate a self-learning algorithm for the computer classification of information extracted from flow cytometric immunophenotype list mode files from high-grade non-Hodgkins lymphoma (NHL), Hodgkins disease (HD), and multiple myeloma (MM). Materials and Methods Bone marrow aspirates (BMA) were obtained from untreated NHL (n = 51), HD (n = 9), or MM (n = 13) patients. Bone marrow aspirates were not infiltrated in NHL and HD patients as confirmed by thorough histologic and cytologic investigation; however, MM patients showed an infiltration rate >50% by malignant myeloma cells. Peripheral blood leukocyte (PBL) samples were taken from age-matched healthy volunteers (n = 44) as easily available control material. A second control group of 15 healthy volunteers, from whom BMA and PBL samples were available, allowed us to differentiate whether the observed classification results on malignant samples were due to the malignant process or simply to the inherent differences between BMA and PBL. Bone marrow aspirates and PBL were analyzed by the same immunophenotyping antibody panel (CD45/14/20, CD4/8/3, kappa/CD19/5, lambda/CD19/5). The acquired list mode data files were analyzed and classified by the self-learning triple matrix classification algorithms CLASSIF1 following a priori separation of the data into a learning set and unknown test set. After completion of the learning phase, known patient samples were reclassified and unknown samples prospectively classified by the algorithm. RESULTS Highly discriminatory information was extracted for the various lymphoma entities. The most discriminating information was encountered in antibody binding, antibody binding ratios, and relative antibody surface density parameters of leukocytes rather than in percentage frequencies of discrete leukocyte subpopulations. Samples from healthy controls were classified as normal in 97.2% of the cases, whereas those of NHL, HD, and MM patients were on average correctly classified in 80. 8% of the cases. CONCLUSIONS Although no detectable lymphoma cells were present in BMA of NHL and HD patients, the CLASSIF1 classification of the immunophenotypes of morphologically normal cells provided a surprisingly good disease discrimination equal or better than that obtained by examining pathological lymph nodes according to the respective literature. The results are suggestive for a lymphoma-related and disease-specific antigen expression shift on normal hematopoietic bone marrow cells that can be used to discriminate the underlying disease (specificity of unspecific changes), i.e., in this case NHL from HD. Multiple myeloma patients were discriminated by changes on malignant as well as on normal bone marrow cells.

Durchflußcytometrische Untersuchungen an 30 Lungen-Carcinomen mit Hilfe automatischer Diagnoseauswertung

Bei Lungencarcinomen finden sich gehauft eine spate Diagnosestellung, eingeschrankte Behandlungsmoglichkeiten und ungunstige Langzeitergebnisse. Daraus ergibt sich der Wunsch, verdachtige Herde so fruh wie moglich uber Punktion, Bronchoskopie, Bronchiallavage oder Sputumuntersuchungen abzuklaren. Die Methode der Durchfluscytometrie ermoglicht eine quantitative Analyse biochemischer und biologischer Parameter auf cellularer Ebene und gestattet damit zusatzlich zur routinemasigen Histopathologie neue, erweiterte Tumorklassifikationen. Mit Hilfe der elektrischen Widerstandsmessung und der Fluorescenzbestimmung konnen im Durchfluscytometer bis zu 1000 Zellen pro Sekunde gleichzeitig auf ihr Zellvolumen, auf ihren DNS-Gehalt, ihren intracellularen pH-Wert und ihre Esteraseaktivitat untersucht werden (1, 2). Der cellulare DNS-Gehalt ist dabei ein wesentlicher Parameter zur Differenzierung zwischen malignen und benignen Geweben. Lassen sich in den DNS-Verteilungskurven abnorme bzw. zusatzliche Gipfel nachweisen, so kann diese DNS-Aneuploidie als sicheres Zeichen fur Malignitat gewertet werden und auch zur automatischen Tumoranalyse verwendet werden (5). Durch die simultane cytometrische Bestimmung von cellularer Esterasekonzentration und Zellvolumen ist eine weitere Unterscheidung benigner und maligner Gewebe moglich (1). Ziel dieser Arbeit war die biochemische und biologische Differenzierung von normalem Lungenepithel und Lungencarcinomen auf cellularer Ebene sowie die multifaktorielle Analyse zur automatischen Diagnosestellung mit Hilfe des neuentwickelten DIAGNOS 1 Programmsystems. Daruber hinaus sollten fur die Tumoren Korrelationen zwischen DNS-Ploidie, histopathologischer Klassifikationen und Prognose uberpruft werden.