International year of Chemistry 2011. A guide to the history of clinical chemistry

Automation in Flow Cytometry

Automation in clinical flow cytometry has the potential to revolutionize the field by improving processes and enhancing efficiency and accuracy. Integrating advanced robotics and artificial intelligence, these technologies can streamline sample preparation, data acquisition, and analysis. Automated sample handling reduces human error and increases throughput, allowing laboratories to handle larger volumes with consistent precision. Intelligent algorithms contribute to rapid data interpretation, aiding in the identification of cellular markers for disease diagnosis and monitoring. This automation not only accelerates turnaround times but also ensures reproducibility, making clinical flow cytometry a reliable tool in the realm of personalized medicine and diagnostics.

Clinical metabolomics for inborn errors of metabolism

Metabolism is a highly regulated process that provides nutrients to cells and essential building blocks for the synthesis of protein, DNA and other macromolecules. In healthy biological systems, metabolism maintains a steady state in which the concentrations of metabolites are relatively constant yet are subject to metabolic demands and environmental stimuli. Rare genetic disorders, such as inborn errors of metabolism (IEM), cause defects in regulatory enzymes or proteins leading to metabolic pathway disruption and metabolite accumulation or deficiency. Traditionally, the laboratory diagnosis of IEMs has been limited to analytical methods that target specific metabolites such as amino acids and acyl carnitines. This approach is effective as a screening method for the most common IEM disorders but lacks the comprehensive coverage of metabolites that is necessary to identify rare disorders that present with nonspecific clinical symptoms. Fortunately, advancements in technology and data analytics has introduced a new field of study called metabolomics which has allowed scientists to perform comprehensive metabolite profiling of biological systems to provide insight into mechanism of action and gene function. Since metabolomics seeks to measure all small molecule metabolites in a biological specimen, it provides an innovative approach to evaluating disease in patients with rare genetic disorders. In this review we provide insight into the appropriate application of metabolomics in clinical settings. We discuss the advantages and limitations of the method and provide details related to the technology, data analytics and statistical modeling required for metabolomic profiling of patients with IEMs.

Automation in the clinical laboratory: integration of several analytical and intralaboratory pre- and post-analytical systems

Clinical laboratory automation is very important to obtain reliable test results and to provide patient safety. There are some difficulties in implementing total automation to the clinical laboratories because they need a continuous, high quality customer service to keep their high quality serving, a questionable cost-affecting situation. It may be very difficult to keep the balance between the cost and the quality goals, patient safety, and demands. However, clinical laboratory automation may solve the dilemma and be implemented in clinical laboratories provided that it does not result in new bottlenecks in laboratory workflow. It is beyond the dispute that the minimal operator intervention benefited by total lab automation results in increased productivity, intra laboratory traceability of specimens, the decreased turnaround times, improvements in specimen handling, improved laboratory safety, and minimized errors. It has become very difficult, time-consuming, challenging task for the laboratories to decide to automate and which tests must be included in the analytical automation, to decide which one is more appropriate. First of all, a workflow and a workload analysis must be made for the present semi-automated laboratory. It would be focused in the present review that some strategies can be developed for this purpose.

Emerging applications of metabolomics in drug discovery and precision medicine

Metabolomics is an emerging 'omics' science involving the comprehensive characterization of metabolites and metabolism in biological systems. Recent advances in metabolomics technologies are leading to a growing number of mainstream biomedical applications. In particular, metabolomics is increasingly being used to diagnose disease, understand disease mechanisms, identify novel drug targets, customize drug treatments and monitor therapeutic outcomes. This Review discusses some of the latest technological advances in metabolomics, focusing on the application of metabolomics towards uncovering the underlying causes of complex diseases (such as atherosclerosis, cancer and diabetes), the growing role of metabolomics in drug discovery and its potential effect on precision medicine.

Proteomic Monitoring of B Cell Immunity

Immune monitoring is critical in settings of infection, autoimmunity, and cancer, but our understanding of the diversity of the antibody immune repertoire has been limited to selected target antigens and epitopes. Development of new vaccines requires monitoring of B cell immunity and identification of candidate antigens. As vaccines become more complex, novel techniques are required for monitoring the diversity of the B cell immune response. Since antibodies recognize both linear and conformational protein and glycoprotein epitopes, recent advances in proteomic and glycomic technologies for rapid display of antigenic structures are leading to methods for proteome-wide immune monitoring. Here, we review different approaches for protein display for immune monitoring, and provide methods for in situ protein display for the rapid detection and validation of antibody repertoires.

The successful implementation of a licensed data management interface between a Sunquest(®) laboratory information system and an AB SCIEX™ mass spectrometer

Background:

Interfacing complex laboratory equipment to laboratory information systems (LIS) has become a more commonly encountered problem in clinical laboratories, especially for instruments that do not have an interface provided by the vendor. Liquid chromatography-tandem mass spectrometry is a great example of such complex equipment, and has become a frequent addition to clinical laboratories. As the testing volume on such instruments can be significant, manual data entry will also be considerable and the potential for concomitant transcription errors arises. Due to this potential issue, our aim was to interface an AB SCIEX™ mass spectrometer to our Sunquest^® LIS.

Materials and Methods:

We licensed software for the data management interface from the University of Pittsburgh, but extended this work as follows: The interface was designed so that it would accept a text file exported from the AB SCIEX™ × 5500 QTrap^® mass spectrometer, pre-process the file (using newly written code) into the correct format and upload it into Sunquest^® via file transfer protocol.

Results:

The licensed software handled the majority of the interface tasks with the exception of converting the output from the Analyst^® software to the required Sunquest^® import format. This required writing of a “pre-processor” by one of the authors which was easily integrated with the supplied software.

Conclusions:

We successfully implemented the data management interface licensed from the University of Pittsburgh. Given the coding that was required to write the pre-processor, and alterations to the source code that were performed when debugging the software, we would suggest that before a laboratory decides to implement such an interface, it would be necessary to have a competent computer programmer available.