Adventage of mesenchymal stem cells (MSC) expansion directly from purified bone marrow CD105^+ and CD271^+ cells

Mesenchymal Stem Cells (MSC) are employed in gene and cellular therapies. Routinely MSC are isolated from bone marrow mononuclear cells (MNC) by plastic adherence. Here we compared new isolation strategies of bone marrow MSC including immunodepletion of hematopoietic cells and immunomagnetic isolation of CD105+ and CD271+ populations. Four fractions were obtained: MNC MSC, RosetteSep-isolated MSC, CD105+ and CD271+ sorted MSC. We evaluated i) number of CFU-F colonies, ii) cell phenotype, iii) in vitro differentiation of expanded cells and iv) expression of osteo/adipogenesis related genes. Results: Average number of day 9 CFU-F colonies was the highest for CD271 positive fraction. Real-Time PCR analysis revealed expression of RUNX2, PPARgamma and N-cadherin in isolated cells, particularly high in CD271+ cells. Expression of CD105, CD166, CD44, CD73 antigens was comparable for all expanded populations (over 90%). We observed various levels of hematopoietic contamination with the highest numbers of CD45+ cells in MNC-MSC fraction and the lowest in CD105+ and CD271+ fractions. Cells of all the fractions were CD34 antigen negative. Expanded CD105 and CD271 populations showed higher level of RUNX2, osteocalcin, PTHR, leptin, PPARgamma2 and aggrecan1 genes except for alpha1 collagen. After osteogenic differentiation CD105+ and CD271+ populations showed lower expression of RUNX, PPARgamma2 and also lower expression of osteocalcin and PTHR than MNC, with comparable alpha1-collagen expression. Chondrogenic and adipogenic gene expression was higher in MNC. More clonogenic CD105+ and particularly CD271+ cells, which seem to be the most homogenous fractions based on Real-Time PCR and immunostaining data, are better suited for MSC expansion

Gene expression down-regulation in CD90+ prostate tumor-associated stromal cells involves potential organ-specific genes

<p>Abstract</p> <p>Background</p> <p>The prostate stroma is a key mediator of epithelial differentiation and development, and potentially plays a role in the initiation and progression of prostate cancer. The tumor-associated stroma is marked by increased expression of CD90/THY1. Isolation and characterization of these stromal cells could provide valuable insight into the biology of the tumor microenvironment.</p> <p>Methods</p> <p>Prostate CD90⁺stromal fibromuscular cells from tumor specimens were isolated by cell-sorting and analyzed by DNA microarray. Dataset analysis was used to compare gene expression between histologically normal and tumor-associated stromal cells. For comparison, stromal cells were also isolated and analyzed from the urinary bladder.</p> <p>Results</p> <p>The tumor-associated stromal cells were found to have decreased expression of genes involved in smooth muscle differentiation, and those detected in prostate but not bladder. Other differential expression between the stromal cell types included that of the CXC-chemokine genes.</p> <p>Conclusion</p> <p>CD90⁺prostate tumor-associated stromal cells differed from their normal counterpart in expression of multiple genes, some of which are potentially involved in organ development.</p

Application of independent component analysis on raman images of a pharmaceutical drug product: pure spectra determination and spatial distribution of constituents

Independent component analysis (ICA) was used as a blind source separation method on a Raman image of a pharmaceutical tablet. Calculations were performed without a priori knowledge concerning the formulation. The aim was to extract the pure signals from the initial data set in order to examine the distribution of actives and major excipients within the tablet. As a method based on the decomposition of a matrix of mixtures of several components, the number of independent component to choose is a critical step of the analysis. The ICA_by_blocks method, based on the calculation of several models using an increasing number of independent components on initial matrix blocks, was used. The calculated ICA signals were compared with the pure spectra of the formulation compounds. High correlations between the two active principal ingredient spectra and their corresponding calculated signals were observed giving a good overview of the distributions of these compounds within the tablet. Information from the major excipients (lactose and avicel) was found in several independent components but the ICA approach provides high level of information concerning their distribution within the tablet. However, the results could vary considerably by changing the number of independent components or the preprocessing method. Indeed, it was shown that under-decomposition of the matrix could lead to better signal quality (compared to the pure spectra) but in that case the contributions due to minor components or effects were not correctly identified and extracted. On the contrary, over-decomposition of the original dataset could provide information about low concentration compounds at the expense of some loss of signal interpretability for the other compounds

Détermination de la distribution d'un composé faiblement dosé dans un comprimé pharmaceutique par "multicarte résolution" d'une image Raman

International audienceIn this work, Raman hyperspectral images and Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) are used to study the distribution of actives and excipients within a pharmaceutical drug product. This article is mainly focused on the distribution of a low dose constituent. Different approaches are compared, using initially filtered or non-filtered data, or using a column-wise augmented dataset before starting the MCR-ALS iterative process including appended information on the low dose component. In the studied formulation, magnesium stearate is used as a lubricant to improve powder flowability. With a theoretical concentration of 0.5% w/w in the drug product, the spectral variance contained in the data is weak. By using a Principal Component Analysis (PCA) filtered dataset as a first step of the MCR-ALS approach, the lubricant information is lost in the non-explained variance and its associated distribution in the tablet cannot be highlighted. A sufficient number of components to generate the PCA noise-filtered matrix has to be used in order to keep the lubricant variability within the data set analyzed or, otherwise, work with the raw non-filtered data. Different models are built using an increasing number of components to perform the PCA reduction. It is shown that the magnesium stearate information can be extracted from a PCA model using a minimum of 20 components. In the last part, a column-wise augmented matrix, including a reference spectrum of the lubricant, is used before starting MCR-ALS process. PCA reduction is performed on the augmented matrix, so the magnesium stearate contribution is included within the MCR-ALS calculations. By using an appropriate PCA reduction, with a sufficient number of components, or by using an augmented dataset including appended information on the low dose component, the distribution of the two actives, the two main excipients and the low dose lubricant are correctly recovered

CCMH élevées : la réponse avec le CBC-O

# 03-22International audienc

Double mutation JAK2L611V/V617F dans un cas pédiatrique de polyglobulie

# 10-30International audienc