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Wide-Field Multi-Parameter FLIM: long-term minimal invasive observation of proteins in living cells.

ABSTRACT: Time-domain Fluorescence Lifetime Imaging Microscopy (FLIM) is a remarkable tool to monitor the dynamics of fluorophore-tagged protein domains inside living cells. We propose a Wide-Field Multi-Parameter FLIM method (WFMP-FLIM) aimed to monitor continuously living cells under minimum light intensity at a given illumination energy dose. A powerful data analysis technique applied to the WFMP-FLIM data sets allows to optimize the estimation accuracy of physical parameters at very low fluorescence signal levels approaching the lower bound theoretical limit. We demonstrate the efficiency of WFMP-FLIM by presenting two independent and relevant long-term experiments in cell biology: 1) FRET analysis of simultaneously recorded donor and acceptor fluorescence in living HeLa cells and 2) tracking of mitochondrial transport combined with fluorescence lifetime analysis in neuronal processes.

SUBMITTER: Vitali M

PROVIDER: S-EPMC3032730 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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Wide-Field Multi-Parameter FLIM: long-term minimal invasive observation of proteins in living cells.

Vitali Marco M Picazo Fernando F Prokazov Yury Y Duci Alessandro A Turbin Evgeny E Götze Christian C Llopis Juan J Hartig Roland R Visser Antonie J W G AJ Zuschratter Werner W

PloS one 20110202 2

Time-domain Fluorescence Lifetime Imaging Microscopy (FLIM) is a remarkable tool to monitor the dynamics of fluorophore-tagged protein domains inside living cells. We propose a Wide-Field Multi-Parameter FLIM method (WFMP-FLIM) aimed to monitor continuously living cells under minimum light intensity at a given illumination energy dose. A powerful data analysis technique applied to the WFMP-FLIM data sets allows to optimize the estimation accuracy of physical parameters at very low fluorescence s ...[more]

PMID: 21311595

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Project description:BackgroundPatients with consistent lung pure ground-glass nodules (pGGNs) have a high incidence of lung adenocarcinoma that can be classified as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). Regular follow-up is recommended for AIS and MIA, while surgical resection should be considered for IAC. This study sought to develop a multi-parameter prediction model to increase the diagnostic accuracy in discriminating between IAC and AIS or MIA.MethodsThe training data set comprised consecutive patients with lung pGGNs who underwent resection from January to December 2017 at the Zhongshan Hospital. Of the 370 resected pGGNs, 344 were pathologically confirmed to be AIS, MIA, or IAC and were included in the study. The 26 benign pGGNs were excluded. We compared differences in the clinical features (e.g., age and gender), the content of serum tumor biomarkers, the computed tomography (CT) parameters (e.g., nodule size and the maximal CT value), and the morphologic characteristics of nodules (e.g., lobulation, spiculation, pleura indentation, vacuole sign, and normal vessel penetration or abnormal vessel) between the pathological subtypes of AIS, MIA, and IAC. An abnormal vessel was defined as "vessel curve" or "vessel enlargement". Statistical analyses were performed using the chi-square test, analysis of variance (ANOVA), and rank test. The IAC prediction model was constructed via a multivariate logistical regression. Our prediction model for lung pGGNs was further validated in a data set comprising consecutive patients from multiple medical centers in China from July to December 2018. In total, 345 resected pGGNs were pathologically diagnosed as lung adenocarcinoma in the validation data set.ResultsIn the training data set, patients with pGGNs ≥10 mm in size had a high incidence (74.5%) of IAC. The maximal CT value of IAC [-416.1±121.2 Hounsfield unit (HU)] was much higher than that of MIA (-507.7±138.0 HU) and AIS (-602.6±93.3 HU) (P<0.001). IAC was more common in pGGNs that displayed any of the following CT manifestations: lobulation, spiculation, pleura indentation, vacuole sign, and vessel abnormality. The IAC prediction model was constructed using the parameters that were assessed as risk factors (i.e., the nodule size, maximal CT value, and CT signs). The receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) of this model for diagnosing IAC was 0.910, which was higher than that of the AUC for nodule size alone (0.891) or the AUC for the maximal CT value alone (0.807) (P<0.05, respectively). A multicenter validation data set was used to validate the performance of our prediction model in diagnosing IAC, and our model was found to have an AUC of 0.883, which was higher than that of the AUC of 0.827 for the module size alone model or the AUC of 0.791 for the maximal CT value alone model (P<0.05, respectively).ConclusionsOur multi-parameter prediction model was more accurate at diagnosing IAC than models that used only nodule size or the maximal CT value alone. Thus, it is an efficient tool for identifying the IAC of malignant pGGNs and deciding if surgery is needed.

Project description:OBJECTIVE:Delayed response to clinical deterioration as a result of intermittent vital sign monitoring is a cause of preventable morbidity and mortality. This review focuses on the clinical impact of multi-parameter continuous non-invasive monitoring of vital signs (CoNiM) in non-intensive care unit patients. DESIGN:Systematic review and meta-analysis of primary studies. Embase, MEDLINE, HMIC, PsycINFO and Cochrane were searched from April 1964 to 18 June 2019 with no language restriction. SETTING:The search was limited to hospitalised, non-intensive care unit adult patients who had two or more vital signs continuously monitored. PARTICIPANTS:All primary studies that evaluated the clinical impact of using multi-parameter CoNiM in adult hospital wards outside of the intensive care unit. MAIN OUTCOME MEASURES:Clinical impact of multi-parameter CoNiM. RESULTS:This systematic review identified 14 relevant studies from 3846 search results. Five studies were classified as Group A - associations found between measured vital signs and clinical parameters. Nine studies were classified as Group B - comparison between clinical outcomes of patients with and without multi-parameter CoNiM. Vital signs data from CoNiM were found to associate with type of presenting complaint, level of renal function and incidence of major clinical events. CoNiM also assisted in diagnosis by differentiating between patients with acute heart failure, stroke and sepsis (with sub-clustering of septic patients). In the meta-analysis, patients on multi-parameter CoNiM had a 39% decrease in risk of mortality (risk ratio [RR] 0.61; 95% confidence interval [95% CI] -0.39-0.95) when compared to patients with regular intermittent monitoring. There was a trend of reduced intensive care unit transfer (RR 0.86; 95% CI -0.67-1.11) and reduced rapid response team activation (RR 0.61; 95% CI 0.26-1.43). A trend towards reduced hospital length of stay was also found using weighted mean difference (WMD -3.32 days; 95% CI -8.82-2.19 days). CONCLUSION:There is evidence of clinical benefit in implementing CoNiM in non-intensive care unit patients. This review supports the use of multi-parameter CoNiM outside of intensive care unit with further large-scale RCTs required to further affirm clinical impact.

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Wide-Field Multi-Parameter FLIM: long-term minimal invasive observation of proteins in living cells.

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Wide-Field Multi-Parameter FLIM: long-term minimal invasive observation of proteins in living cells.

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