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PubMed2025

Acoustic ejection mass spectrometry: the potential for personalized medicine.

INTRODUCTION: The emergence of personalized medicine (PM) has shifted the focus of healthcare from the traditional 'one-size-fits-all' approach to strategies tailored to individual patients, accounting for genetic, environmental, and lifestyle factors. Acoustic ejection mass spectrometry (AEMS) is a novel technology that offers a robust and scalable platform for high-throughput MS readout. AEMS achieves analytical speeds of one sample per second while maintaining high data quality, broad compound coverage, and minimal sample preparation, making it an invaluable tool for PM. AREAS COVERED: This article explores the potential of AEMS in critical PM applications, including therapeutic drug monitoring (TDM), proteomics, metabolomics, and mass spectrometry imaging. AEMS simplifies conventional workflows by minimizing sample preparation, enhancing automation compatibility, and enabling direct analysis of complex biological matrices. EXPERT OPINION: Integrating AEMS with orthogonal separation techniques such as differential mobility spectrometry (DMS) further addresses challenges in isomer discrimination, expanding the platform's analytical capabilities. Additionally, the development of high-throughput data processing tools could further enable AEMS to accelerate the development of personalized medicine.

Humans↗
PubMed2005

Time-motion analysis of 6 cystic fibrosis mutation detection systems.

BACKGROUND: A dramatic increase in requests for routine cystic fibrosis (CF) carrier screening prompted us to conduct a time-motion analysis comparing commercially available CF testing platforms. Questions addressed in the study included: (a) How much time is required to perform each step involved in carrying out the assay procedure? (b) Which system requires the minimum number of manual manipulations to complete a typical run? (c) What workflow benefits can be achieved by automation? METHODS: We used a 96-sample run for comparisons and analyzed each of the 6 methods to determine the number of pipetting steps and manual manipulations, the labor and instrument time, and the total time required to perform the assay. The survey participants included a staff of 4 technologists who perform complex molecular assays regularly. Time required for each procedure was determined by direct observation and from work logs completed by the technologists. RESULTS: The total number of pipetting motions varied from 78 to 344. Labor time ranged from 2.6 to 8.4 h, and total assay time from 7.6 to 13.7 h. CONCLUSION: Time-motion analysis allowed identification of a method that minimized pipetting motions and thus reduced the risk of repetitive stress injury.

Clinical Laboratory Techniques↗
PubMed2025

Unlocking Zeptomolar Single-Molecule Detection by Synergizing Digital Microfluidics and Digital CRISPR.

Accurate diagnosis relies on the highly sensitive and quantitative detection of multiple immune-related biomarkers. However, current detection methods still face significant limitations in sensitivity, specificity, and background signal control. Here, we introduce DDA (Dual-Digital immunoAssay), a fully automated, universal immunoassay platform that synergizes digital microfluidics with digital Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based amplification. This "dual-digital" strategy pushes the detection limit into the zeptomolar (zM) regime, enabling unprecedented sensitivity for single-molecule analysis. The DDA platform is built upon a digital microfluidic microwell array chip, integrating magnetic bead-based immunocapture with RNA-guided CRISPR/Cas13a signal amplification. This system enables a fully automated, "sample-in, answer-out" workflow. By systematically optimizing the entire process, DDA significantly reduces background noise and enhances detection sensitivity, achieving a limit of detection (LOD) down to 100 zM for key protein biomarkers. This represents a >100-fold improvement over leading commercial ultrasensitive assays. With single-molecule resolution and full automation, DDA provides a robust solution for the precise quantification of low-abundance immune biomarkers. As a proof-of-concept, we demonstrate its ability to accurately quantify key heart-failure-associated biomarkers, including NT-proBNP (LOD: 1 aM), IL-6 (LOD: 1.5 aM), and TNF-α (LOD: 2.5 aM), directly in complex serum samples. This platform holds great promise for automated multibiomarker screening and risk assessment, showcasing its powerful potential for the early diagnosis of major diseases such as cardiovascular diseases, cancers, neurodegenerative disorders, and infectious diseases.

Humans↗
PubMed2003

Workflow-enabled distributed component-based information architecture for digital medical imaging enterprises.

Few information systems today offer a flexible means to define and manage the automated part of radiology processes, which provide clinical imaging services for the entire healthcare organization. Even fewer of them provide a coherent architecture that can easily cope with heterogeneity and inevitable local adaptation of applications and can integrate clinical and administrative information to aid better clinical, operational, and business decisions. We describe an innovative enterprise architecture of image information management systems to fill the needs. Such a system is based on the interplay of production workflow management, distributed object computing, Java and Web techniques, and in-depth domain knowledge in radiology operations. Our design adapts the approach of "4+1" architectural view. In this new architecture, PACS and RIS become one while the user interaction can be automated by customized workflow process. Clinical service applications are implemented as active components. They can be reasonably substituted by applications of local adaptations and can be multiplied for fault tolerance and load balancing. Furthermore, the workflow-enabled digital radiology system would provide powerful query and statistical functions for managing resources and improving productivity. This paper will potentially lead to a new direction of image information management. We illustrate the innovative design with examples taken from an implemented system.

Computer Communication Networks↗
PubMed2003

Advances in high content screening for drug discovery.

Cell-based target validation, secondary screening, lead optimization, and structure-activity relationships have been recast with the advent of HCS. Prior to HCS, a computational approach to the characterization of the functions of specific target proteins and other cellular constituents, along with whole-cell functions employing fluorescence cell-based assays and microscopy, required extensive interaction among the researcher, instrumentation, and software tools. Early HCS platforms were instrument-centric and addressed the need to interface fully automated fluorescence microscopy, plate-handling automation, and seamless image analysis. HCS has since evolved into an integrated solution for accelerated drug discovery by encompassing the workflow components of assay and reagent design, robust instrumentation for automated fixed-end-point and live cell kinetic analysis, generalized and specific BioApplication software (Cellomics, Pittsburgh, PA) modules that produce information on drug responses from cell image data, and informatics/bioinformatics solutions that build knowledge from this information while providing a means to globalize HCS throughout an entire organization. This review communicates how these recent advances are incorporated into the drug discovery workflow by presenting a real-world use case.

Drug Design↗
PubMed2005

Web services and workflow management for biological resources.

BACKGROUND: The completion of the Human Genome Project has resulted in large quantities of biological data which are proving difficult to manage and integrate effectively. There is a need for a system that is able to automate accesses to remote sites and to "understand" the information that it is managing in order to link data properly. Workflow management systems combined with Web Services are promising Information and Communication Technologies (ICT) tools. Some have already been proposed and are being increasingly applied to the biomedical domain, especially as many biology-related Web Services are now becoming available. Information on biological resources and on genomic sequences mutations are two examples of very specialized datasets that are useful for specific research domains. RESULTS: The architecture of a system that is able to access and execute predefined workflows is presented in this paper. Web Services allowing access to the IARC TP53 Mutation Database and CABRI catalogues of biological resources have been implemented and are available on-line. Example workflows which retrieve data from these Web Services have also been created and are available on-line. CONCLUSION: We present a general architecture and some building blocks for the implementation of a system that is able to remotely execute workflows of biomedical interest and show how this approach can effectively produce useful outputs. The further development and implementation of Web Services allowing access to an exhaustive set of biomedical databases and the creation of effective and useful workflows will improve the automation of in-silico analysis.

Animals↗
PubMed2026

CoSAG-nf: A Scalable Nextflow Pipeline for Co-assembly, Optimization, and Interactive Visualization of High-Throughput Single-Cell Genomes.

MOTIVATION: Single-cell amplified genomes (SAGs) are crucial for resolving intra-population microbial heterogeneity and accurately understanding the metabolic potential of microbial dark matter populations. However, SAGs generated through multiple displacement amplification (MDA) of genomic DNA from single cells with single-copy chromosomes are highly fragmented and prone to contamination, severely hindering high-quality genome reconstruction and functional analysis, which greatly limits their scientific utility. Co-assembly of related SAGs can substantially improve genome quality, but to our knowledge no automated pipeline exists for high-throughput processing, forcing manual implementation of complex workflows that scale poorly to modern dataset sizes. RESULTS: We present CoSAG-nf, an automated high-throughput co-assembly and optimization pipeline for SAGs, implemented following the nf-core framework standards. The pipeline performs alignment-free clustering using sourmash MinHash signatures, then employs iterative tetranucleotide frequency profiling to identify and exclude outlier SAGs from co-assembly groups. CheckM2 quality assessment guides dynamic selection of optimal SAG combinations to optimize genome completeness and minimize contamination. Fully containerized, CoSAG-nf ensures reproducibility and scalability for the high-throughput processing of large-scale SAG datasets across diverse computing environments, including HPC and cloud platforms. The pipeline generates comprehensive HTML reports with quality metrics and taxonomic annotations, providing an end-to-end solution for automated high-throughput single-cell genome reconstruction. AVAILABILITY: CoSAG-nf is freely available under the MIT License at: https://github.com/linfengxu/CoSAG-nf. Archival code repository snapshots are published at zenodo with doi: https://doi.org/10.5281/zenodo.21525244. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Journal Article↗
PubMed2006

Technical innovations for the automated identification of gel-separated proteins by MALDI-TOF mass spectrometry.

The combination of gel-based two-dimensional protein separations with protein identification by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) is the workhorse for the large-scale analyses of proteomes. Such high-throughput proteomic approaches require automation of all post-separation steps and the in-gel digest of proteins especially is often the bottleneck in the protein identification workflow. With the objective of reaching the same high performance of manual low-throughput in-gel digest procedures, we have developed a novel stack-type digestion device and implemented it into a commercially available robotic liquid handling system. This modified system is capable of performing in-gel digest, extraction of proteolytic peptides, and subsequent sample preparation for MALDI-MS without any manual intervention, but with a performance at least identical to manual procedures as indicated on the basis of the sequence coverage obtained by peptide mass fingerprinting. For further refinement of the automated protein identification workflow, we have also developed a motor-operated matrix application device to reproducibly obtain homogenous matrix preparation of high quality. This matrix preparation was found to be suitable for the automated acquisition of both peptide mass fingerprint and fragment ion spectra from the same sample spot, a prerequisite for high confidence protein identifications on the basis of peptide mass and sequence information. Due to the implementation of the stack-type digestion device and the motor-operated matrix application device, the entire platform works in a reliable, cost-effective, and sensitive manner, yielding high confidence protein identifications even for samples in the concentration range of as low as 100 fmol protein per gel plug.

Automation↗
PubMed2025

MRDagent: iterative and adaptive parameter optimization for stable ctDNA-based MRD detection in heterogeneous samples.

MOTIVATION: Minimal residual disease (MRD) as critical biomarker for cancer prognosis and management plays a crucial role in improving patient outcomes. However, detecting MRD via next-generation sequencing-based circulating tumor DNA variant calling remains unstable due to the extremely low variant allele frequency and significant inter- and intra-sample heterogeneity. Although parameter optimization can theoretically enhance the detection performance of variants, achieving stable MRD detection remains challenging due to three key factors: (i) the necessity for individualized parameter tuning across numerous heterogeneous genomic intervals within each sample, (ii) the tightly interdependent parameter requirements across different stages of variant detection workflows, and (iii) the limitations of current automated parameter optimization methods. RESULTS: In this study, we propose MRDagent, a novel variant detection tool designed specifically for MRD detection. MRDagent incorporates an iterative and self-adaptive optimization framework capable of handling unknown objectives, varying constraints, and highly coupled parameters across stages. A key innovation of MRDagent is the integration of a convolutional neural network-based meta-model, trained on historical data to enable rapid parameter prediction. This significantly enhances computational efficiency and generalization performance. Extensive evaluations on simulated and real-world datasets demonstrate MRDagent's superior and stable performance, providing an efficient, reliable solution for MRD detection in clinical and high-throughput research applications. AVAILABILITY AND IMPLEMENTATION: MRDagent is freely available at https://github.com/aAT0047/MRDagent.git. The corresponding dataset and software archive are available at Zenodo: https://doi.org/10.5281/zenodo.15458496.

Circulating Tumor DNA↗
PubMed2006

eHiTS: an innovative approach to the docking and scoring function problems.

Virtual Ligand Screening (VLS) has become an integral part of the drug design process for many pharmaceutical companies. In protein structure based VLS the aim is to find a ligand that has a high binding affinity to the target receptor whose 3D structure is known. This review will describe the docking tool eHiTS. eHiTS is an exhaustive and systematic docking tool which contains many automated features that simplify the drug design workflow. A description of the unique docking algorithm and novel approach to scoring used within eHiTS is presented. In addition a validation study is presented that demonstrates the accuracy and wide applicability of eHiTS in re-docking bound ligands into their receptors.

Algorithms↗
PubMed1997

New tools for laboratory design and management.

The clinical laboratory is changing from a place of activity based on sample analysis to an in vitro diagnostic network. To convince our team, partners, and administrators, we need new comprehensive tools to define a strategy with limited risk of failure or conflicts. Specific quality goals should be established before choosing automated tools for sample handling, analytical systems, laboratory information systems, communication systems, or advanced technologies. A system approach maps and simplifies the process, based more on a functional study than on classical disciplines. A customer-supplier approach establishes the requirements between partners either inside or outside the laboratory. The quality system must be a management tool, linking samples, tasks, information, and documents. Quantitative simulation modeling explores different automation alternatives and their impact on laboratory workflow. Finally, integration of results in interactive semirealistic simulation tools for laboratory design or reengineering can be used as communications tools to involve laboratory professionals in the change of their practice.

Chemistry, Clinical↗
PubMed2025

Tractor Workflow Pipeline: A Scalable Nextflow Framework for Local Ancestry-Aware Genome-Wide Association Studies.

The routine exclusion of admixed individuals from traditional Genome-Wide Association Studies (GWAS) due to concerns about spurious associations has hindered genetic analyses involving multiple ancestries. Tractor GWAS addresses this issue by incorporating local ancestry into its analysis, empowering identification of ancestry-enriched hits and generating ancestry-specific summary statistics. However, Tractor requires accurate genomic phasing and local ancestry inference as prerequisite steps, which requires additional bioinformatics expertise and decision points regarding reference panel setup. To streamline, harmonize, and automate this process, we present a scalable Nextflow workflow that integrates all necessary steps, minimizing the need for manual intervention while remaining modular and customizable. The workflow supports multiple commonly used tools and offers flexibility in how Tractor is implemented. To demonstrate its utility, we applied this pipeline to analyze 32 blood biomarkers in 6,245 two-way AFR-EUR admixed individuals from the UK Biobank. This pipeline ran efficiently at scale, replicated known associations, and identified novel ancestry-specific loci. These novel associations were largely driven by variants present on African ancestral tracts but absent from European tracts, underscoring the value of local ancestry-aware methods in uncovering previously missed genetic signals. By enabling the efficient analysis of admixed individuals, our workflow facilitates Tractor use, paving the way for more broader genetic discovery.

Journal Article↗
PubMed2005

Analysis of the comparative workflow and performance characteristics of the VITEK 2 and Phoenix systems.

The VITEK 2 (bioMérieux, Marcy L'Etoile, France) and the Phoenix systems (BD Diagnostic Systems, Sparks, Md.) are automated instruments for rapid organism identification and susceptibility testing. We evaluated the workflow, the time to result, and the performance of identification and susceptibility testing of both instruments. A total of 307 fresh clinical isolates were tested: 141 Enterobacteriaceae, 22 nonfermenters, 93 Staphylococcus spp., and 51 Enterococcus spp. Manipulation time was measured in batches, each with seven isolates, for a total of 39 batches. The mean (+/- standard deviation [SD]) manipulation time per batch was 20.9 +/- 1.8 min for Phoenix and 10.6 +/- 1.0 min for VITEK 2 (P < 0.001). Mean (+/-SD) time to result for all bacterial groups was 727 +/- 162 min for Phoenix and 506 +/- 120 min for VITEK 2 (P < 0.001). Concerning identification, Phoenix and VITEK 2 yielded the same results for nonfermenters (100%), staphylococci (97%), and enterococci (100%). For 140 Enterobacteriaceae strains evaluated, 135 (96%) were correctly identified by Phoenix and 137 (98%) by VITEK 2 (P = 0.72). The overall category agreement for all isolates was 97.0% for both instruments. The minor error rate, major error rate, and very major error rate for all bacterial isolates tested were 3.0, 0.3, and 0.6 and 2.8, 0.2, and 1.7 for Phoenix and VITEK 2, respectively (P values of 0.76, 0.75, and 0.09). The VITEK 2 system required less manual manipulation time and less time than the Phoenix system to yield results.

Automation↗
PubMed2001

Creation of a software application for the clinical radiologist.

PURPOSE: To create a software application that automates a significant portion of a clinical radiologist's work. METHODS: The daily workflow of clinical radiologists in a university-based department was analyzed, specifically looking for manual tasks that could be implemented in software. An application that a practicing radiologist would use in his daily work was created. RESULTS: By using speech recognition, reports could be created, edited, and verified immediately. Online medical reference information could be made instantly accessible. Billing data could be captured accurately and completely at report creation time. A radiologist could be alerted to important upcoming events by use of a reminder system. Reports could be delivered immediately to referring physicians using either the internet or telephone. CONCLUSIONS: It is possible to improve the productivity of busy practicing clinical radiologists by automating a large portion of their workflow and implementing it in an easily mastered software application.

Humans↗
PubMedDate not supplied

Setup for high-throughput impedance screening of gas-sensing materials.

This paper reports on the setup for a high-throughput impedance measurement system that allows rapid screening of the electrical and dielectrical properties of solid-state sample libraries in variable atmospheres and temperatures. Using multielectrode arrays, most time-consuming steps in the workflow are parallelized. In addition, an approach for automated data evaluation of impedance spectra is presented. For reasons of verification of robust measuring results and reproducibility, screening results of a sample library composed of doped indium(III) oxide as a resistive-type gas-sensing material are discussed on the basis of the determined sensitivities focusing temperature and testing gas gradients.

Journal Article↗
PubMedDate not supplied

Case study: improving efficiency in a large hospital laboratory.

Saint Francis Health System (SFHS) consists of three hospitals and one clinic: Saint Francis Hospital (SFH); Broken Arrow Medical Center; Laureate Psychiatric Hospital; and Warren Clinic. SFHS has 670 physicians on staff and serves medical (oncology, orthopedic, neurology, and renal), surgical, cardiac, women and infant, pediatric, transplant, and trauma patients in Tulsa County, Oklahoma, which has a population of 660,000. SFH incorporates 706 staffed beds, including 126 pediatric beds and 119 critical care beds. Each year, the health system averages 38,000 admissions, 70,000 emergency department visits, 25,000 surgeries, and 3,500 births. Saint Francis Laboratory is located within the main hospital facility (SFH) and functions as a core lab for the health system. The lab also coordinates lab services with Saint Francis Heart Hospital, a physician-system joint venture. The Optimal Equipment Configuration (OEC) Project was designed by the Clinical Laboratory Services division of Premier, a group purchasing organization, with the goal of determining whether laboratories could improve efficiency and decrease unit cost by using a single-source vendor. Participants included seven business partners (Abbott, Bayer, Beckman/Coulter, Dade/Behring, J&J/ Ortho, Olympus, and Roche) and 21 laboratory sites (a small, mid-sized, and large site for each vendor). SFH laboratory staff embraced Premier's concept and viewed the OEC project as an opportunity to "energize" laboratory operations. SFH partnered with Abbott, their primary equipment vendor, for the project. Using resources and tools made available through the project, the laboratory was re-engineered to simplify workflow, increase productivity, and decrease costs by adding automation and changing to centralized specimen processing. Abbott and SFH shared a common vision for the project and enhanced their partnership through increased communication and problem solving. Abbott's area representatives provided for third-party design expertise and quarterly metric reporting through Argent Consulting. Abbott incorporated lessons learned from the SFH OEC project with organizational changes to improve the way they work with customers. Following is a step-by-step description of the OEC project to allow others to benefit from the experience (Figure 1).

Efficiency, Organizational↗
PubMed2004

Automating proteome analysis: improvements in throughput, quality and accuracy of protein identification by peptide mass fingerprinting.

The use of robots has major effects on maximizing the proteomic workflow required in an increasing number of high-throughput projects and on increasing the quality of the data. In peptide mass finger printing (PMF), automation of steps downstream of two-dimensional gel electrophoresis is essential. To achieve this goal, the workflow must be fluid. We have developed tools using macros written in Microsoft Excel and Word to complete the automation of our platform. Additionally, because sample preparation is crucial for identification of proteins by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry, we optimized a sandwich method usable by any robot for spotting digests on a MALDI target. This procedure enables further efficient automated washing steps directly on the MALDI target. The success rate of PMF identification was evaluated for the automated sandwich method, and for the dried-droplet method implemented on the robot as recommended by the manufacturer. Of the two methods, the sandwich method achieved the highest identification success rate and sequence coverage of proteins.

Colonic Neoplasms↗
PubMed2025

LncRAnalyzer: a robust workflow for long non-coding RNA discovery using RNA-Seq.

Long non-coding RNA (lncRNA) is a major transcript category that lacks protein-coding capabilities, with relatively low abundance and complex expression patterns. Distinguishing lncRNAs from protein-coding genes is a complex process involving multiple filtering steps. We developed an automated pipeline named LncRAnalyzer featuring retrained models for 60 species. This workflow aims to reduce the likelihood of obtaining protein-coding or partial protein-coding transcripts during lncRNA identification by utilizing eight distinct approaches. We conducted a 10-fold cross-validation of the sorghum models and training sets with their standard ones and other approaches using real-life RNA-Seq datasets and known lncRNA and CDS sequences of sorghum. The results showed that the sorghum models and training sets were outperformed. The pipeline output comprises upset plots illustrating the number of lncRNA/NPCTs identified by the approaches, commonly identified lncRNA and their classes, NPCTs, and expression count tables. A feature-level comparison and benchmarking analysis of LncRAnalyzer with four existing pipelines, namely, LncPipe, LncEvo, lncRNA-Annotation, and Plant-LncPipe, demonstrated that LncRAnalyzer is more comprehensive, easier to implement, and accurate in lncRNA predictions. This workflow also ascertains lncRNA origins from various Transposable Elements (TEs) in plants using TE annotations from APTEdb [http://apte.cp.utfpr.edu.br/]. LncRAnalyzer is publicly available on GitLab [https://gitlab.com/nikhilshinde0909/LncRAnalyzer.git] for academic users.

RNA, Long Noncoding↗