Diagnosis Of The Chronic Lymphocytic Leukemia (CLL) Using A Raman-Based Scanner Optimized For Blood Smear Analysis (M3s Project)

Introduction/ Background In hematology, actual diagnosis of B chronic lymphocyte-leukemia (CLL) is based on the microscopic analysis of cell morphology from patient blood smear. However, new photonic technologies appear promising to facilitate and improve the early diagnosis, prognostic and monitoring of personalized therapy. The development of automated diagnostic approaches could assist clinicians in improving the efficiency and quality of health services, but also reduce medical costs. Aims The M3S project aims at improving the diagnosis and prognosis of the CLL pathology by developing a multimodal microscopy platform, including Raman spectrometry, dedicated to the automatic analysis of lymphocytes. Methods Blood smears were prepared on glass slides commonly used in pathology laboratories for microscopy. Two types of sample per patient were prepared: a conventional blood smear and a deposit of “pure” lymphocyte subtypes (i.e. normal B, CLL B, T and NK), sorted out in flow cytometry by using the negative double labeling technique. The second sample is used for the construction of a database of spectral markers specific of these different cell types. The preparations were analyzed with the multimodal machine which combines i) a Raman micro-spectrometer, equipped with a 532nm diode laser excitation source; ii) a microscope equipped with 40x and 150x lenses and a high precision xyz motorized stage for scanning the blood smear, and localizing x-y coordinates of representative series (~100 for each patient) of lymphocyte cells before registering three Raman spectra; these cells of interest being previously localized by an original method based on the morphology analysis. After the Raman acquisitions, the conventional blood smears were submitted to immunolabelling using specific antibodies. For the establishment of the Raman classifiers, this post-acquisition treatment was used as reference to distinguish the different lymphocyte sub-populations. Raman data were then analyzed using chemometric processing and supervised statistical classifiers in order to construct a spectral library of markers highly specific of the lymphocyte type and status (normal or pathological). Results Currently, a total of 60 patients (CLL and healthy) were included in the study. Various classification methods such as LDA (Linear Discriminant Analysis), PLS-DA (Partial Least Square Discriminant Analysis), RF (Random Forest) and SVM (Support Vector Machine), were tested in the purpose to distinguish tumoral B lymphocytes from other cell types. These classification algorithms were combined with feature selection approaches. The best performances were around 70% of correct identification when a three-class model (B-CLL vs B-normal vs T and NK lymphocytes) was considered, and 80% in case of a two-class model (B-CLL vs B-normal lymphocytes). These encouraging results demonstrate the potential of Raman micro-spectroscopy coupled to supervised classification algorithms for leukemic cell classification. The approach can find interest more generally in the field of cyto-hematology. Further developments will concern the integration of additional modality such as Quantitative Phase Imaging on one hand to speed the exploration process of cells of interest to be probed, and on the other hand to extract additional characteristics likely to be informative for CLL diagnosis. In addition, the identification of prognostic markers will be investigated by confronting the photonic data to clinical patient information.

Application Of Raman Microscopy For The Diagnosis Of The Chronic Lymphocytic Leukemia (Cll) : PS01.01 | ePoster Session I

INTRODUCTION / BACKGROUND: In hematology, actual diagnosis of B chronic lymphocyte-leukemia (CLL) is based on the microscopic analysis of cell morphology from patient blood smear. However, new photonic technologies appear promising to facilitate and improve the early diagnosis, prognostic and monitoring of personalized therapy. The development of automated diagnostic approaches could assist clinicians in improving the efficiency and quality of health services, but also reduce medical costs. AIMS: The M3S project aims at improving the diagnosis and prognosis of the CLL pathology by developing a multimodal microscopy platform, including Raman spectrometry, dedicated to the automatic analysis of lymphocytes. METHODS: Blood smears were prepared on glass slides commonly used in pathology laboratories for microscopy. Two types of sample per patient were prepared: a conventional blood smear and a deposit of “pure” lymphocyte subtypes (i.e. normal B, CLL B, T and NK), sorted out in flow cytometry by using the negative double labelling technique. The second sample is used for the construction of a database of spectral markers specific of these different cell types. The preparations were analyzed with the multimodal machine which combines i) a Raman micro-spectrometer, equipped with a 532nm diode laser excitation source; ii) a micro- scope equipped with 40x and 150x lenses and a high precision xyz motorized stage for scanning the blood smear, and localizing x-y coordinates of representative series (~100 for each patient) of lymphocyte cells before registering three Raman spectra; these cells of interest being previously localized by an original method based on the morphology analysis. After the Raman acquisitions, the conventional blood smears were submitted immunolabelling using specific antibodies. For the establishment of the Raman classifiers, this post-acquisition treatment was used as reference to distinguish the different lymphocyte sub-populations. Raman data were then analyzed using chemometric processing and supervised statistical classifiers in order to construct a spectral library of markers highly specific of the lymphocyte type and status (normal or pathological). RESULTS: Currently, a total of 60 patients (CLL and healthy) were in- cluded in the study. Various classification methods such as LDA (Linear Discriminant Analysis), PLS-DA (Partial Least Square Discriminant Analysis), RF (RandomForest) and SVM (Support Vector Machine), were tested in the purpose to distinguish tumoral B lymphocytes from other cell types. These classification algorithms were combined with feature selection approaches. The best performances were around 70% of correct identification when a three-class model (B-CLL vs B-normal vs T and NK lymphocytes) was considered, and 80% in case of a two-class model (B-CLL vs B-normal lymphocytes). These encouraging results demonstrate the potential of Raman micro-spectroscopy coupled to supervised classification algorithms for leukemic cell classification. The approach can find interest more generally in the field of cyto-hematology. Further developments will concern the integration of additional modality such as Quantitative Phase Imaging on one hand to speed the exploration process of cells of interest to be probed, and on the other hand to extract additional characteristics likely to be informative for CLL diagnosis. In addition, the identification of prognostic markers will be investigated by confronting the photonic data to clinical patient information

Structural and biological control of the Cenozoic epithermal uranium concentrations from the Sierra Pena Blanca, Mexico

Epithermal uranium deposits of the Sierra Peña Blanca are classic examples of volcanic-hosted deposits and have been used as natural analogs for radionuclide migration in volcanic settings. We present a new genetic model that incorporates both geochemical and tectonic features of these deposits, including one of the few documented cases of a geochemical signature of biogenic reducing conditions favoring uranium mineralization in an epithermal deposit. Four tectono-magmatic faulting events affected the volcanic pile. Uranium occurrences are associated with breccia zones at the intersection of fault systems. Periodic reactivation of these structures associated with Basin and Range and Rio Grande tectonic events resulted in the mobilization of U and other elements by meteoric fluids heated by geothermal activity. Focused along breccia zones, these fluids precipitated under reducing conditions several generations of pyrite and uraninite together with kaolinite. Oxygen isotopic data indicate a low formation temperature of uraninite, 45-55°C for the uraninite from the ore body and ~20°C for late uraninite hosted by the underlying conglomerate. There is geochemical evidence for biological activity being at the origin of these reducing conditions, as shown by low δ 34S values (~-24. 5‰) in pyrites and the presence of low δ 13C (~-24‰) values in microbial patches intimately associated with uraninite. These data show that tectonic activity coupled with microbial activity can play a major role in the formation of epithermal uranium deposits in unusual near-surface environments