Author: Nanofcm Date: May 26, 2022
Introduction
Cellular autofluorescence in the visible region can affect the sensitivity of fluorescence microscopic or flow cytometric assays by interfering with or even precluding the detection of low-level specific fluorescence. On the other side, the detection of autofluorescence can provide information for bacterial discrimination and identification. The autofluorescence detected in the green region may have originated from flavins, which comprise a category of molecules that include riboflavin (RF, vitamin B2) and its derivatives flavin adenine dinucleotide (FAD) and flavin mononucleotide (FMN). The oxidized form of flavins all shares remarkably similar spectral characteristics with fluorescein isothiocyanate (FITC). Three yellow-green fluorescent Fluospheres beads with different sizes and known fluorescein equivalents were analyzed in parallel with bacterial samples to construct the calibration curve between the mean fluorescence burst area and the FITC equivalents per nanoparticles. Then the burst area distribution histogram of bacterial autofluorescence was converted to the distribution of FITC equivalents per bacterial cell.
Instrument configuration
The Flow NanoAnalyzer is equipped with a 488 nm CW laser, and the detection channels are side scatter and green fluorescence (FITC).
Results
Figure 1. Detection of three fluorescent nanoparticles with different FITC equivalents.
Figure 2. Bivariate dot plots of autofluorescence burst area versus side scatter burst area for eight different bacteria.
Discussion
Anal. Chem. 2012, 84, 1526-1532.
Introduction
Single-cell analysis is vital in providing insights into the heterogeneity in molecular content and phenotypic characteristics of complex or clonal cell populations. As many essential proteins and most transcription factors are produced at a low copy number, analytical tools with superior sensitivity to enable the analysis of low abundance proteins in single cells are in high demand. b-galactosidase (b-gal) has been the standard cellular reporter for gene expression in both prokaryotic and eukaryotic cells. Here Flow NanoAnalyzer is used for the development of a high-throughput method for the single-cell analysis of low copy number b-gal proteins. Upon fluorescence staining with a fluorogenic substrate C12FDG, quantitative measurements of the basal and near-basal expression of b-gal in single bacteria is demonstrated. Combined with the quantitative fluorometric assay and the rapid bacterial enumeration, the b-gal expression distribution profile could be converted from arbitrary fluorescence units to protein copy numbers per cell.
Instrument configuration
The Flow NanoAnalyzer is equipped with a 488 nm CW laser, and the detection channels are side scatter and green fluorescence (FITC).
Results
Figure 1. Analysis of basal b-gal expression in single bacterial cells.
Figure 2. Quantitative analysis of b-gal.
Table 1. Quantification of b-gal copy numbers expressed in single bacterial cells.
*: Average number of b-gal molecules per cell is determined by the quantitative MUG fluorometric assay integrated with rapid bacterial enumeration on the HSFCM.
**: a and b are the two parameters characterizing the gamma distribution. The mean (n) and standard deviation (s ) of the protein number distribution are calculated from a and b.
Discussion
Biosens. Bioelectron. 2013, 48, 49-56.
Introduction
Bacterial resistance to antibiotics poses a great clinical challenge in fighting serious infectious diseases due to complicated resistant mechanisms and time-consuming test methods. Among many molecular mechanisms that confer antibiotic resistance, the production of b-lactamase that catalyze the hydrolysis of b-lactam antibiotics is a major and threatening mechanism. On the other hand, it has been reported that individuals could be simultaneously infected with multiple strains of different susceptibility levels. The traditional detection method cannot detect the minority population of antibiotic-resistant bacteria. Advanced tools are urgently needed to quickly diagnose antibiotic-resistant infections to initiate appropriate treatment. The hydrolyzed probes LBRL1 could attach the enzyme, b-lactamase, and thus facilitated the covalent labeling of drug-resistant bacterial strains. Moreover, this b-lactamase-induced covalent labeling provides a quantitative analysis of the resistant bacterial population (down to 5%) by Flow NanoAnalyzer.
Instrument configuration
The Flow NanoAnalyzer is equipped with a 488 nm CW laser, and the detection channels are side scatter and green fluorescence (FITC).
Results
Figure 1. Analysis of bacteria resistance in single gram-negative bacterial cell. (Bacteria are labeled with LBRL1, E. coli JM109 (green), E. coli JM109/pUC19 (red), inhibitor treated E. coli JM109/pUC19 (blue))
Figure 2. Analysis of bacteria resistance in single gram-positive bacterial cell. (Unlabeled B. cereus (green), LBRL1 labeled B. cereus (red), inhibitor treated B. cereus (blue))
Figure 3. Differentiation of resistant E. coli JM109/pUC19 cells in bacterial mixtures.
Discussion
Chem. Eur. J. 2013, 19, 10903-10910.
Introduction
It has been reported that individuals could be simultaneously infected with multiple strains of different susceptibility levels, and the population of resistant bacteria could be very low. However, if the minority population of resistant bacteria cannot be detected in time, an inappropriate prescription of antibiotics is usually a result. Therefore, the detection minority population of antibiotic-resistant bacteria is very important for clinical diagnosis. Employing monoclonal antibody against TEM-1 b-lactamase and Alexa Fluor 488-conjugated secondary antibody to selectively label resistant bacteria green, and nucleic acid dye SYTO 62 to stain all the bacteria, Flow NanoAnalyzer is able to detect and quantify as low as 0.1% of antibiotic-resistant bacteria. Furthermore, this approach is applied to detect antibiotic-resistant infection in clinical urine samples without cultivation, and the bacterial load of susceptible and resistant strains can be faithfully quantified. This method provides a powerful tool for the fundamental studies of antibiotic resistance and holds the potential to provide rapid and precise guidance in clinical therapies.
Instrument configuration
The Flow NanoAnalyzer is equipped with a 488 nm CW laser, and the detection channels are side scatter, green fluorescence (FITC) and red fluorescence (APC).
Results
Figure 1. Track the dynamic population change of antibiotic-resistant bacteria with and without antibiotics.
Figure 2. Analysis of E. coli ATCC 35218 (positive control) and two b-lactamase positive clinical urine samples upon dual fluorescent staining.
Discussion
Biosen. Bioelectron. 2016, 80, 323-330.
Introduction
A safe and secure supply of drinking water is an essential requirement for human health. Because many different microbiological contaminants may occur in drinking water and beverages, the total bacterial count represents one of the key parameters for quality assessment. Currently, water dispenser is fairly commonly used, the fluctuation of water quality, especially the bacterial count of the unsealed barreled water has attracted much attention. The cultivation-based heterotrophic plate count (HPC) has long become a firmly established tool for the assessment of water quality, but it is labour-intensive, time-consuming, and of limited usage in certain circumstances. On the other hand, bacteria that are in a state of very low metabolic activity, such as those viable but non-cultivable (VBNC) bacteria, can be overlooked by HPC. Employing nucleic acid dye PicoGreen to label the particles in water, particles show burst traces in both side scatter and fluorescent channels simultaneously are recognized as bacteria. Compared with HPC, Flow NanoAnalyzer based approach not only shortens the analysis time but also reveals the presence of dead and VBNC bacterial cells.
Instrument configuration
The Flow NanoAnalyzer is equipped with a 488 nm CW laser, and the detection channels are side scatter and green fluorescence (FITC).
Results
Figure 1. Total bacterial quantification of bottled/barreled drinking water.
Figure 2. Total bacterial quantification in Jasmine Green Tea drinks.
Discussion
Anal. Methods 2015, 7, 3072-3079.
Introduction
Identification and quantification of infectious disease agents are important for medical diagnosis, public health, food safety, environmental monitoring, and anti bioterrorism. However, traditional culture-based methods are laborious, time-consuming, and only suitable for viable and cultivable cells. Though flow cytometry is emerging as one of the best choices for microbe quantification, its applications to bacteria detection is frequently hindered by bacteria’s small sizes and consequently the low contents of specific cellular constituents. Here Alexa Fluor 647-R-PE is used as the fluorescent probe for the monoclonal antibody of pathogenic E. coli O157:H7, the green fluorophore SYTO 9 is used to stain all the bacterial cells. Double-stained E. coli O157:H7, can be specifically identified and enumerated using two-color fluorescence coincidence detection, while non-pathogenic bacteria can be quantified by green fluorescence detection.
Instrument configuration
The Flow NanoAnalyzer is equipped with a 488 nm CW laser, and the detection channels are side scatter, green fluorescence (FITC) and red fluorescence (APC).
Results
Figure 1. Analysis of a double-stained mixture with the percentage of E. coli O157:H7/total bacterial cells of 51/100.
Discussion