The trace analysis of microorganisms in real samples by combination of a filtration microcartridge and capillary isoelectric focusing

Determination of Pathogens by Electrophoretic and Spectrometric Techniques

In modern medical diagnostics, where analytical chemistry plays a key role, fast and accurate identification of pathogens is becoming increasingly important. Infectious diseases pose a growing threat to public health due to population growth, international air travel, bacterial resistance to antibiotics, and other factors. For instance, the detection of SARS-CoV-2 in patient samples is a key tool to monitor the spread of the disease. While there are several techniques for identifying pathogens by their genetic code, most of these methods are too expensive or slow to effectively analyze clinical and environmental samples that may contain hundreds or even thousands of different microbes. Standard approaches (e.g., culture media and biochemical assays) are known to be very time- and labor-intensive. The purpose of this review paper is to highlight the problems associated with the analysis and identification of pathogens that cause many serious infections. Special attention was paid to the description of mechanisms and the explanation of the phenomena and processes occurring on the surface of pathogens as biocolloids (charge distribution). This review also highlights the importance of electromigration techniques and demonstrates their potential for pathogen pre-separation and fractionation and demonstrates the use of spectrometric methods, such as MALDI-TOF MS, for their detection and identification.

Preparative and capillary isoelectric focusing for detection and identification of Aspergillus conidia in complex sample matrices

This study describes a new method for fast identification of highly hydrophobic conidia of Aspergillus species from both simple and complex matrices. The method is based on recently developed preparative isoelectric focusing in a cellulose-based separation medium which had to be modified with respect to the highly hydrophobic surface of the conidia. Although Aspergillus conidia are colored, their zones in the cellulose bed were indicated by colored isoelectric point markers. The isoelectric point values of Aspergillus conidia were determined by capillary isoelectric focusing. Preparative isoelectric focusing was successfully used for preconcentration of individual conidia of cultivated strains of Aspergillus niger, Aspergillus fumigatus, Aspergillus flavus, and Aspergillus parasiticus, and also for separation of the conidia in a mixture. Subsequently, red pepper powder and peanuts spiked with Aspergillus niger and Aspergillus flavus conidia, respectively, were used as complex matrices. The detection limit for identification of the conidia in these complex matrices is 10⁴ conidia mL^-1 . The presence of conidia in the focused zones was confirmed by their subsequent analysis by capillary isoelectric focusing. Their viability was confirmed by a cultivation of the conidia extracted from the collected fractions after preparative isoelectric focusing.

Predicting isoelectric points of nonfunctional mitochondria from Monte Carlo simulations of surface compositions

Mitochondria are heterogeneous organelles involved in energy production, metabolism, and cellular signaling that oftentimes are isolated from cells for chemical characterization (e.g., proteomic analysis). The chemical composition of the mitochondrial outer membrane is one of the factors defining the mitochondrial isoelectric point (pI), which is a property useful for the analysis and characterization of isolated mitochondria. We previously used capillary isoelectric focusing (cIEF) with laser-induced-fluorescence detection to determine the experimental pI of individual mitochondria after their isolation under depolarizing conditions. This technique revealed that, when kept nonfunctional, mitochondrial pI is heterogeneous as displayed by the observed distributions of pI. To model the effect of surface composition on pI heterogeneity of these mitochondria, we devised a method to predict mitochondrial pI values using simulated surface compositions. The method was initially validated by predicting the pI values of known mitochondrial outer membrane proteins and was then extended to isolated mitochondria, in which both ionizable amino acids and phospholipids contribute to mitochondrial pI. After using a Monte Carlo method to generate a library of over 2 million possible mitochondrial surface compositions, sufficient compositions to match the frequency of occurrence of experimental mitochondrial pI values were randomly selected. This comparison allows for association of a given individual mitochondrial pI with thousands of randomly chosen compositions. The method predicts significant changes in the percentages of some amino acids and phospholipids for observed pI differences between individual mitochondria, which is an important advancement toward explaining the observed heterogeneity of mitochondrial pI.