Switchable and Functional Fluorophores for Multidimensional Single-Molecule Localization Microscopy.

Multidimensional single-molecule localization microscopy (mSMLM) represents a paradigm shift in the realm of super-resolution microscopy techniques. It affords the simultaneous detection of single-molecule spatial locations at the nanoscale and functional information by interrogating the emission properties of switchable fluorophores. The latter is finely tuned to report its local environment through carefully manipulated laser illumination and single-molecule detection strategies. This Perspective highlights recent strides in mSMLM with a focus on fluorophore designs and their integration into mSMLM imaging systems. Particular interests are the accomplishments in simultaneous multiplexed super-resolution imaging, nanoscale polarity and hydrophobicity mapping, and single-molecule orientational imaging. Challenges and prospects in mSMLM are also discussed, which include the development of more vibrant and functional fluorescent probes, the optimization of optical implementation to judiciously utilize the photon budget, and the advancement of imaging analysis and machine learning techniques.

An automated system for rapid non-destructive enumeration of growing microbes.

BACKGROUND: The power and simplicity of visual colony counting have made it the mainstay of microbiological analysis for more than 130 years. A disadvantage of the method is the long time required to generate visible colonies from cells in a sample. New rapid testing technologies generally have failed to maintain one or more of the major advantages of culture-based methods. PRINCIPAL FINDINGS: We present a new technology and platform that uses digital imaging of cellular autofluorescence to detect and enumerate growing microcolonies many generations before they become visible to the eye. The data presented demonstrate that the method preserves the viability of the microcolonies it detects, thus enabling generation of pure cultures for microbial identification. While visual colony counting detects Escherichia coli colonies containing about 5x10(6) cells, the new imaging method detects E. coli microcolonies when they contain about 120 cells and microcolonies of the yeast Candida albicans when they contain only about 12 cells. We demonstrate that digital imaging of microcolony autofluorescence detects a broad spectrum of prokaryotic and eukaryotic microbes and present a model for predicting the time to detection for individual strains. Results from the analysis of environmental samples from pharmaceutical manufacturing plants containing a mixture of unidentified microbes demonstrate the method's improved test turnaround times. CONCLUSION: This work demonstrates a new technology and automated platform that substantially shortens test times while maintaining key advantages of the current methods.

Identification of Enterobacter sakazakii from closely related species: the use of artificial neural networks in the analysis of biochemical and 16S rDNA data.

BACKGROUND: Enterobacter sakazakii is an emergent pathogen associated with ingestion of infant formula and accurate identification is important in both industrial and clinical settings. Bacterial species can be difficult to accurately characterise from complex biochemical datasets and computer algorithms can potentially simplify the process. RESULTS: Artificial Neural Networks were applied to biochemical and 16S rDNA data derived from 282 strains of Enterobacteriaceae, including 189 E. sakazakii isolates, in order to identify key characteristics which could improve the identification of E. sakazakii. The models developed resulted in a predictive performance for blind (validation) data of 99.3 % correct discrimination between E. sakazakii and closely related species for both phenotypic and genotypic data. Three main regions of the partial rDNA sequence were found to be key in discriminating the species. Comparison between E. sakazakii and other strains also constitutively positive for expression of the enzyme alpha-glucosidase resulted in a predictive performance of 98.7 % for 16S rDNA sequence data and 100% for phenotypic data. CONCLUSION: The computationally based methods developed here show a remarkable ability in reducing data dimensionality and complexity, in order to eliminate noise from the system in order to facilitate the speed and reliability of a potential strain identification system. Furthermore, the approaches described are also able to provide valuable information regarding the population structure and distribution of individual species thus providing the foundations for novel assays and diagnostic tests for rapid identification of pathogens.

Description of Campylobacter curvus and C. curvus-like strains associated with sporadic episodes of bloody gastroenteritis and Brainerd's diarrhea.

Campylobacter curvus is a rarely encountered Campylobacter species in human, animal, and environmental samples. During the course of two investigations, one involving a search for possible bacterial agents causing bloody gastroenteritis and a second concerning a small outbreak of Brainerd's diarrhea in northern California, 20 strains of C. curvus or C. curvus-like organisms were isolated by a microfiltration technique and prolonged incubation. The results suggest that C. curvus may be an underappreciated Campylobacter that may be involved in sporadic and outbreak cases of bloody or chronic diarrhea in humans.

Identification and phylogeny of Enterobacter sakazakii relative to Enterobacter and Citrobacter Species.

The phylogenetic relationships of Enterobacter sakazakii strains were investigated using 16S ribosomal DNA (rDNA) and hsp60 sequencing. Each analysis distributed E. sakazakii strains among four clusters, indicating substantial taxonomic heterogeneity. The E. sakazakii type strain 16S rDNA sequence was 97.8% similar to that of Citrobacter koseri but 97.0% similar to that of Enterobacter cloacae.