Sub-Cellular Metabolomics Contributes Mitochondria-Specific Metabolic Insights to a Mouse Model of Leigh Syndrome

Unlocking the Mitochondria for Nanomedicine-based Treatments: Overcoming Biological Barriers, Improving Designs, and Selecting Verification Techniques

Enhanced targeting approaches will support the treatment of diseases associated with dysfunctional mitochondria, which play critical roles in energy generation and cell survival. Obstacles to mitochondria-specific targeting include the presence of distinct biological barriers and the need to pass through (or avoid) various cell internalization mechanisms. A range of studies have reported the design of mitochondrially-targeted nanomedicines that navigate the complex routes required to influence mitochondrial function; nonetheless, a significant journey lies ahead before mitochondrially-targeted nanomedicines become suitable for clinical use. Moving swiftly forward will require safety studies, in vivo assays confirming effectiveness, and methodologies to validate mitochondria-targeted nanomedicines' subcellular location/activity. From a nanomedicine standpoint, we describe the biological routes involved (from administration to arrival within the mitochondria), the features influencing rational design, and the techniques used to identify/validate successful targeting. Overall, rationally-designed mitochondria-targeted-based nanomedicines hold great promise for precise subcellular therapeutic delivery.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.