Preservation of Fluorescent Protein Activity in Tumor Tissue

Rosenberg, Elizabeth; Holmes, Matthew; Tenenholz, T.C.; Khalil, Ashraf; Valerie, K.

doi:10.2144/03343bm05

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…It is likely that these changes in scatter, a crude measure of cell size, reflect changes in the optical properties of RNAlater-treated specimens. Similar increases in the background fluorescence of RNAlater treated specimens has been observed by other groups [9,20]. We also studied the effects of RNAlater on a GFP positive T cell line [21].…”

Section: Resultssupporting

confidence: 66%

Use of RNAlater in fluorescence-activated cell sorting (FACS) reduces the fluorescence from GFP but not from DsRed

et al. 2010

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BackgroundFlow cytometry utilizes signals from fluorescent markers to separate targeted cell populations for gene expression studies. However, the stress of the FACS process could change normal gene expression profiles. RNAlater could be used to stop such changes in original gene expression profiles through its ability to denature RNase and other proteins. The normal conformational structure of fluorescent proteins must be maintained in order to fluoresce. Whether or not RNAlater would affect signals from different types of intrinsic fluorescent proteins is crucial to its use in flow cytometry; this question has not been investigated in detail.FindingsTo address this question, we analyzed the effect of RNAlater on fluorescence intensity of GFP, YFP, DsRed and small fluorescent molecules attached to secondary antibodies (Cy2 and Texas-Red) when used in flow cytometry. FACS results were confirmed with fluorescence microscopy. Our results showed that exposure of YFP and GFP containing cells to RNAlater reduces the intensity of their fluorescence to such an extent that separation of such labeled cells is difficult if not impossible. In contrast, signals from DsRed2, Cy2 and Texas-Red were not affected by RNAlater treatment. In addition, the background fluorescence and clumping of dissociated cells are altered by RNAlater treatment.ConclusionsWhen considering gene expression studies using cell sorting with RNAlater, DsRed is the fluorescent protein of choice while GFP/YFP have severe limitations because of their reduced fluorescence. It is necessary to examine the effects of RNAlater on signals from fluorescent markers and the physical properties (e.g., clumping) of the cells before considering its use in cell sorting.

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Section: Resultssupporting

confidence: 66%

Use of RNAlater in fluorescence-activated cell sorting (FACS) reduces the fluorescence from GFP but not from DsRed

et al. 2010

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“…Multicellular communities were scraped from the TSBMg plates and immediately resuspended in RNAlater (Qiagen) in 1.5 ml RNAse-free Eppendorf tubes, in order to fix the cell fluorescence and at the same time preserve the RNA within the cells. Previous reports ( Rosenberg et al, 2003 ) and fluorescence microscopy experiments performed in our laboratory (data not shown) showed that the fixing procedure of these multicellular communities in RNAlater had no effect in the conservation of the fluorescence when compared to cells fixed using 4% paraformaldehyde. Multicellular communities were disrupted in the RNAlater by extensive pipetting, followed by one series of mild sonication as mentioned above, and previously treating the sonicator with RNaseZap RNase Decontamination Solution (Life Technologies) (RRID: SCR_008817 ).…”

Section: Methodsmentioning

confidence: 51%

Cell differentiation defines acute and chronic infection cell types in Staphylococcus aureus

García-Betancur

Goñi-Moreno

Horger

et al. 2017

eLife

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A central question to biology is how pathogenic bacteria initiate acute or chronic infections. Here we describe a genetic program for cell-fate decision in the opportunistic human pathogen Staphylococcus aureus, which generates the phenotypic bifurcation of the cells into two genetically identical but different cell types during the course of an infection. Whereas one cell type promotes the formation of biofilms that contribute to chronic infections, the second type is planktonic and produces the toxins that contribute to acute bacteremia. We identified a bimodal switch in the agr quorum sensing system that antagonistically regulates the differentiation of these two physiologically distinct cell types. We found that extracellular signals affect the behavior of the agr bimodal switch and modify the size of the specialized subpopulations in specific colonization niches. For instance, magnesium-enriched colonization niches causes magnesium binding to S. aureusteichoic acids and increases bacterial cell wall rigidity. This signal triggers a genetic program that ultimately downregulates the agr bimodal switch. Colonization niches with different magnesium concentrations influence the bimodal system activity, which defines a distinct ratio between these subpopulations; this in turn leads to distinct infection outcomes in vitro and in an in vivo murine infection model. Cell differentiation generates physiological heterogeneity in clonal bacterial infections and helps to determine the distinct infection types.

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“…Improved imaging of tumors has revolutionized noninvasive monitoring of distribution, growth, metastasis and morphometrics of tumor models in mice. Available systems include micro positron electromagnetic imaging (Ray et al, 2003;Yang et al, 2003), magnetic resonance imaging (Berr et al, 2003;Nelson et al, 2003), in situ visualization of primary and metastatic tumors and occult metastases (Menon and Teicher, 2002), using improved fluorochromes (Rosenberg et al, 2003) and quantum dots (Watson et al, 2003), green fluorescent protein (GFP) (Hoffman, 2002), and luciferase (Edinger et al, 1999;Burgos et al, 2003). Improved fluorochromes, GFP, and LacZ (Kruger et al, 1999;Culp et al, 2001) are also retained within tissues and allow microscopic evaluation by fluorescent microscopy and histochemical or immunohistochemical staining of tumor tissues.…”

Section: Technical Trends In Tumor Modelingmentioning

confidence: 99%

Trials, Tribulations, and Trends in Tumor Modeling in Mice

Schuh¹

2004

Toxicol Pathol

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Selection of mouse models of cancer is often based simply on availability of a mouse strain and a known compatible tumor. Frequently this results in use of tumor models long on history but short on homology and quality control. Other factors including genetics, sex, immunological status, method and site of tumor implantation, technical competence, biological activity of the tumor, protocol sequence and timing, and selection of endpoints interact to produce outcomes in tumor models. Common reliance on survival and tumor burden data in a single mouse model often skews expectations towards high remission and cure rates; a finding seldom duplicated in clinical trials. Inherent limitations of tumor models coupled with the advent of new therapeutic targets reinforce need for careful attention to design, conduct, and stringent selection of in vivo and ex vivo endpoints. Preclinical efficacy testing for anti-tumor therapies should progress through a series of models of increasing sophistication that includes incorporation of genetically engineered animals, and orthotopic and combination therapy models. Pharmacology and safety testing in tumor-bearing animals may also help to improve predictive value of these models for clinical efficacy. Trends in bioinformatics, genetic refinements, and specialized imaging techniques are helping to maintain mice as the most scientifically and economically powerful model of malignant neoplasms.

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Preservation of Fluorescent Protein Activity in Tumor Tissue

Cited by 5 publications

References 8 publications

Use of RNAlater in fluorescence-activated cell sorting (FACS) reduces the fluorescence from GFP but not from DsRed

Use of RNAlater in fluorescence-activated cell sorting (FACS) reduces the fluorescence from GFP but not from DsRed

Cell differentiation defines acute and chronic infection cell types in Staphylococcus aureus

Trials, Tribulations, and Trends in Tumor Modeling in Mice

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