Literature DB >> 18349285

Effect of tissue shipping on plasma cell isolation, viability, and RNA integrity in the context of a centralized good laboratory practice-certified tissue banking facility.

Gregory J Ahmann1, Wee Joo Chng, Kimberly J Henderson, Tammy L Price-Troska, Roberta W DeGoey, Michael M Timm, Angela Dispenzieri, Philip R Greipp, Alicia Sable-Hunt, Leif Bergsagel, Rafael Fonseca.   

Abstract

The Multiple Myeloma Research Consortium has established a tissue bank for the deposition of bone marrow samples from patients with multiple myeloma to be mailed and processed under good laboratory practices. To date, over 1,000 samples have been collected. At this time, limited information is available on shipped bone marrow aspirates in regards to cell viability, yield, purity, and subsequent RNA yield and quality. To test these determinants, we did a pilot study on behalf of the Multiple Myeloma Research Consortium where samples were drawn at Mayo Clinic Rochester (MCR) pooled and split into two equal aliquots. One-half of each sample was processed following good laboratory practices compliant standard operating procedures, immediately after sample procurement, at MCR. The CD138+ cells were stored at -80 degrees C as a Trizol lysate. The other half of the aspirate was sent overnight to Mayo Clinic Scottsdale where they were processed using identical standard operating procedures. The RNA was extracted and analyzed in a single batch at MCR. At both locations, samples were assayed for the following quality determinants: Viability was assessed using a three-color flow cytometric method (CD45, CD38, and 7-AAD). Cell counts were done to determine plasma cell recovery and post-sort purity determined by means of a slide-based immunofluorescent assay. RNA recovery and integrity was assessed using the Agilent Bioanalyzer. Lastly, gene expression profiles were compared to determine the signature emanating from the shipment of samples. Despite minor differences, our results suggest that shipment of samples did not significantly affect these quality determinants in aggregate.

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Year:  2008        PMID: 18349285     DOI: 10.1158/1055-9965.EPI-07-2649

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN: 1055-9965            Impact factor:   4.254


  9 in total

1.  Variation of Peripheral Blood Mononuclear Cell RNA Quality in Archived Samples.

Authors:  Zisis Kozlakidis; Christine Mant; Fartun Abdinur; Andrew Cope; Szabi Steiner; Mark Peakman; Adrian Hayday; John Cason
Journal:  Biopreserv Biobank       Date:  2011-09       Impact factor: 2.300

Review 2.  What we mean when we talk about MRD in myeloma. A review of current methods. Part 1 of a two-part series.

Authors:  Scott Ely; Noa Biran; Ajai Chari
Journal:  Curr Hematol Malig Rep       Date:  2014-12       Impact factor: 3.952

3.  Methods for culturing human femur tissue explants to study breast cancer cell colonization of the metastatic niche.

Authors:  Zachary S Templeton; Michael H Bachmann; Rajiv V Alluri; William J Maloney; Christopher H Contag; Bonnie L King
Journal:  J Vis Exp       Date:  2015-03-15       Impact factor: 1.355

4.  Optimization of immunomagnetic selection of myeloma cells from bone marrow using magnetic activated cell sorting.

Authors:  Jana Cumova; L Kovarova; A Potacova; I Buresova; F Kryukov; M Penka; J Michalek; R Hajek
Journal:  Int J Hematol       Date:  2010-08-07       Impact factor: 2.490

5.  Hypodiploid multiple myeloma is characterized by more aggressive molecular markers than non-hyperdiploid multiple myeloma.

Authors:  Scott Van Wier; Esteban Braggio; Angela Baker; Gregory Ahmann; Joan Levy; John D Carpten; Rafael Fonseca
Journal:  Haematologica       Date:  2013-05-28       Impact factor: 9.941

6.  siRNA targeting the κ light chain constant region: preclinical testing of an approach to nonfibrillar and fibrillar light chain deposition diseases.

Authors:  X Ma; P Zhou; S W Wong; M Warner; C Chaulagain; R L Comenzo
Journal:  Gene Ther       Date:  2016-06-20       Impact factor: 5.250

7.  Multiple myeloma acquires resistance to EGFR inhibitor via induction of pentose phosphate pathway.

Authors:  Yan Chen; Ruibin Huang; Jianghua Ding; Dexiang Ji; Bing Song; Liya Yuan; Hong Chang; Guoan Chen
Journal:  Sci Rep       Date:  2015-04-20       Impact factor: 4.379

8.  Profound impact of sample processing delay on gene expression of multiple myeloma plasma cells.

Authors:  Tobias Meißner; Anja Seckinger; Kari Hemminki; Uta Bertsch; Asta Foersti; Mathias Haenel; Jan Duering; Hans Salwender; Hartmut Goldschmidt; Gareth J Morgan; Dirk Hose; Niels Weinhold
Journal:  BMC Med Genomics       Date:  2015-12-30       Impact factor: 3.063

9.  Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy.

Authors:  Jens G Lohr; Petar Stojanov; Scott L Carter; Peter Cruz-Gordillo; Michael S Lawrence; Daniel Auclair; Carrie Sougnez; Birgit Knoechel; Joshua Gould; Gordon Saksena; Kristian Cibulskis; Aaron McKenna; Michael A Chapman; Ravid Straussman; Joan Levy; Louise M Perkins; Jonathan J Keats; Steven E Schumacher; Mara Rosenberg; Gad Getz; Todd R Golub
Journal:  Cancer Cell       Date:  2014-01-13       Impact factor: 31.743

  9 in total

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