Comparison of MALDI-TOF mass spectra with microsatellite length polymorphisms in Candida albicans

Nosocomial transmission of Aspergillus flavus in a neonatal intensive care unit: Long-term persistence in environment and interest of MALDI-ToF mass-spectrometry coupled with convolutional neural network for rapid clone recognition

Aspergillosis of the newborn remains a rare but severe disease. We report four cases of primary cutaneous Aspergillus flavus infections in premature newborns linked to incubators contamination by putative clonal strains. Our objective was to evaluate the ability of matrix-assisted laser desorption/ionisation time of flight (MALDI-TOF) coupled to convolutional neural network (CNN) for clone recognition in a context where only a very small number of strains are available for machine learning. Clinical and environmental A. flavus isolates (n = 64) were studied, 15 were epidemiologically related to the four cases. All strains were typed using microsatellite length polymorphism. We found a common genotype for 9/15 related strains. The isolates of this common genotype were selected to obtain a training dataset (6 clonal isolates/25 non-clonal) and a test dataset (3 clonal isolates/31 non-clonal), and spectra were analysed with a simple CNN model. On the test dataset using CNN model, all 31 non-clonal isolates were correctly classified, 2/3 clonal isolates were unambiguously correctly classified, whereas the third strain was undetermined (i.e., the CNN model was unable to discriminate between GT8 and non-GT8). Clonal strains of A. flavus have persisted in the neonatal intensive care unit for several years. Indeed, two strains of A. flavus isolated from incubators in September 2007 are identical to the strain responsible for the second case that occurred 3 years later. MALDI-TOF is a promising tool for detecting clonal isolates of A. flavus using CNN even with a limited training set for limited cost and handling time.

Evaluation of Microsatellite Typing, ITS Sequencing, AFLP Fingerprinting, MALDI-TOF MS, and Fourier-Transform Infrared Spectroscopy Analysis of Candida auris

Candida auris is an emerging opportunistic yeast species causing nosocomial outbreaks at a global scale. A few studies have focused on the C. auris genotypic structure. Here, we compared five epidemiological typing tools using a set of 96 C. auris isolates from 14 geographical areas. Isolates were analyzed by microsatellite typing, ITS sequencing, amplified fragment length polymorphism (AFLP) fingerprint analysis, matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS), and Fourier-transform infrared (FTIR) spectroscopy methods. Microsatellite typing grouped the isolates into four main clusters, corresponding to the four known clades in concordance with whole genome sequencing studies. The other investigated typing tools showed poor performance compared with microsatellite typing. A comparison between the five methods showed the highest agreement between microsatellite typing and ITS sequencing with 45% similarity, followed by microsatellite typing and the FTIR method with 33% similarity. The lowest agreement was observed between FTIR spectroscopy, MALDI-TOF MS, and ITS sequencing. This study indicates that microsatellite typing is the tool of choice for C. auris outbreak investigations. Additionally, FTIR spectroscopy requires further optimization and evaluation before it can be used as an epidemiological typing method, comparable with microsatellite typing, as a rapid method for tracing nosocomial fungal outbreaks.

Geena 2, improved automated analysis of MALDI/TOF mass spectra

Background

Mass spectrometry (MS) is producing high volumes of data supporting oncological sciences, especially for translational research. Most of related elaborations can be carried out by combining existing tools at different levels, but little is currently available for the automation of the fundamental steps.

For the analysis of MALDI/TOF spectra, a number of pre-processing steps are required, including joining of isotopic abundances for a given molecular species, normalization of signals against an internal standard, background noise removal, averaging multiple spectra from the same sample, and aligning spectra from different samples.

In this paper, we present Geena 2, a public software tool for the automated execution of these pre-processing steps for MALDI/TOF spectra.

Results

Geena 2 has been developed in a Linux-Apache-MySQL-PHP web development environment, with scripts in PHP and Perl. Input and output are managed as simple formats that can be consumed by any database system and spreadsheet software. Input data may also be stored in a MySQL database. Processing methods are based on original heuristic algorithms which are introduced in the paper.

Three simple and intuitive web interfaces are available: the Standard Search Interface, which allows a complete control over all parameters, the Bright Search Interface, which leaves to the user the possibility to tune parameters for alignment of spectra, and the Quick Search Interface, which limits the number of parameters to a minimum by using default values for the majority of parameters.

Geena 2 has been utilized, in conjunction with a statistical analysis tool, in three published experimental works: a proteomic study on the effects of long-term cryopreservation on the low molecular weight fraction of serum proteome, and two retrospective serum proteomic studies, one on the risk of developing breat cancer in patients affected by gross cystic disease of the breast (GCDB) and the other for the identification of a predictor of breast cancer mortality following breast cancer surgery, whose results were validated by ELISA, a completely alternative method.

Conclusions

Geena 2 is a public tool for the automated pre-processing of MS data originated by MALDI/TOF instruments, with a simple and intuitive web interface. It is now under active development for the inclusion of further filtering options and for the adoption of standard formats for MS spectra.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0911-2) contains supplementary material, which is available to authorized users.

Pushing the Limits of MALDI-TOF Mass Spectrometry: Beyond Fungal Species Identification

Matrix assisted laser desorption ionization time of flight (MALDI-TOF) is a powerful analytical tool that has revolutionized microbial identification. Routinely used for bacterial identification, MALDI-TOF has recently been applied to both yeast and filamentous fungi, confirming its pivotal role in the rapid and reliable diagnosis of infections. Subspecies-level identification holds an important role in epidemiological investigations aimed at tracing virulent or drug resistant clones. This review focuses on present and future applications of this versatile tool in the clinical mycology laboratory.