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PubMed2006

Integration of multidimensional chromatographic protein separations with a combined "top-down" and "bottom-up" proteomic strategy.

In this paper, we present a combined top-down/bottom-up proteomic analysis workflow for the characterization of proteomic samples. This workflow combines protein fractionation (multidimensional chromatographic separation) with parallel online ESI-TOF-MS intact protein analysis, and fraction collection. Collected fractions were digested and protein identifications were produced using MALDI Q-TOF-MS analysis. These identifications were then linked with corresponding ESI-TOF-MS intact protein mass data to permit full protein characterization. This methodology was applied to an E. coli cytosolic protein fraction, and enabled the identification and characterization of proteins exhibiting co-translational processing, post-translational modification, and proteolytic processing events. The approach also provided the ability to distinguish between closely related protein isoforms. The summary of results from this study indicated that roughly one-third of all detected components generated corresponding data from both top-down and bottom-up analyses, and that significant and novel information can be derived from this application of the hybrid analytical methodology.

Chemical Fractionation↗

PubMed1998

The Camtronics experience with the filmless digital catheterization laboratory.

Systems based on the Archium Digital Cardiac System architecture are providing filmless operation for cardiac catheterization departments in over 80 institutions today. Filmless operation provides direct cost savings from the elimination of cine film as well as its development and management. In addition to these savings, benefits are being realized from productivity associated with changes in the workflow in the department. Image quality and processing capability consistent with the image quality and processing available in the cath lab have proven to be key components in changing workflow and improving efficiency. Solutions are now available which can deliver this level of performance for most departments including multiple lab departments with cath labs from different manufacturers. With Archium, physician productivity can be enhanced with the immediate availability of studies outside the lab and the ability to consult online. Cath lab turnover can be improved significantly. Staff productivity is realized from improved image management as well. The Archium's modular architecture has already accommodated system evolution without obsolescence of existing systems.

Cardiac Catheterization↗

PubMed2026

Efficient prime editing in vivo and in vitro using lipid nanoparticles.

Prime editing is a versatile clinical genome editing method that enables precise substitutions, small insertions and deletions at specified locations in the genomes of living systems including human cells. Although non-viral lipid nanoparticle (LNP) delivery of RNA in vivo has become a preferred method for gene editing in animals and patients, its application to complex, three-component prime editing systems has yielded low editing efficiencies. Here we developed a systematic prime editing LNP (PE-LNP) optimization platform that addresses key bottlenecks in cargo design that limit editing efficiency. This generalizable workflow yielded PE-LNPs that can achieve 49% average in vivo prime editing in the bulk mouse liver with a single dose of 2 mg kg-1. We applied our workflow to the correction of PAH R408W, a cause of phenylketonuria, in a mouse model and achieved prime editing efficiencies and serum phenylalanine levels anticipated to be curative. We also show that PE-LNPs minimize off-target editing compared with DNA delivery methods, induce only transient elevation of liver enzymes and can be dosed repeatedly to improve editing efficiencies. These PE-LNP systems provide an attractive alternative to viral delivery by offering transient expression that minimizes off-target editing, no observed long-term toxicity and high levels of non-viral in vivo liver prime editing.

Animals↗

PubMed2026

Reimagining research papers as interactive and reliable AI agents.

Here we introduce Paper2Agent, an automated framework that converts research papers into artificial intelligence (AI) agents. Paper2Agent transforms research output from passive artefacts into active systems that accelerate use and discovery. Conventional research papers require readers to understand and adapt the paper's code, data and methods to their work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by converting a paper into an AI agent that functions as a virtual corresponding author, exposing its manuscript, supplementary materials, datasets, code and workflows as active, agent-native knowledge rather than static text. It analyses the paper and codebase using multiple agents to construct a model context protocol (MCP) server, then generates and runs tests to refine and increase robustness of the MCP. These paper MCPs can be connected to a chat agent (such as Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the paper. We demonstrate Paper2Agent's effectiveness through case studies. Paper2Agent created an agent that leveraged AlphaGenome1 to interpret genomic variants and agents based on Scanpy2 and TISSUE (transcript imputation with spatial single-cell uncertainty estimation)3 to conduct single-cell and spatial transcriptomics analyses. We validate that these agents reproduce the results of the original papers and carry out novel user queries. Paper2Agent created multiple agents that collaborate to prioritize a causal gene for psoriasis. By turning static papers into interactive AI agents, Paper2Agent introduces a paradigm for knowledge dissemination and a collaborative ecosystem of AI co-scientists.

Journal Article↗

PubMed2026

Volumetric DNA microscopy for mapping spatial transcriptomes in three dimensions.

The architecture and function of biological systems are inherently three-dimensional, yet most existing spatial transcriptomic technologies remain restricted to thin tissue sections, limiting their capacity to resolve cellular organization and microenvironments within intact tissue volumes. To address this limitation, we developed volumetric DNA microscopy, a scalable, optics-free approach for spatial transcriptome profiling directly within intact biological specimens. The method encodes spatial information into DNA molecules that form a dense intermolecular network in situ, enabling the reconstruction of three-dimensional spatial relationships through short-read sequencing and computational analysis. Here we detail the complete workflow including in situ cDNA synthesis, spatial encoding through DNA nanoball formation, dual-scale proximity bridging between neighboring nanoballs and spatial reconstruction via geodesic spectral embedding. Sequencing libraries can be generated within 7-8 d by a competent graduate-level molecular biologist, followed by standardized downstream computational analysis. Because the workflow requires only routine molecular biology reagents and a benchtop sequencer, volumetric DNA microscopy provides a versatile platform for exploring genetic and morphological features in intact tissues.

Spatial Transcriptomics↗

PubMed2026

T-rex: standardized analysis of germline variants in whole-exome sequencing trios.

Whole-exome sequencing (WES) enables the identification of rare germline variants contributing to pediatric diseases. Trio-based sequencing, comparing affected children with their parents, is particularly effective for rare disease genetics. However, WES data analysis requires bioinformatics expertise, varies across institutions, and is often incompatible with clinical workflows. We developed T-Rex (Trio Rare variant analysis of EXomes), a cross-platform desktop application that enables the standardized and local analysis of WES germline Trio data without the need for programming knowledge. T-Rex integrates state-of-the-art tools for alignment, dual-variant calling (GATK HaplotypeCaller + VarScan2), annotation (SNPEff/SNPSift), rare-variant filtering based on population frequencies (gnomAD), and family-based statistical testing, including the Transmission Disequilibrium Test with multiple-testing correction. Benchmarking of the dual-caller strategy on the Genome in a Bottle Ashkenazim Trio demonstrates high precision (99.2%) while maintaining robust sensitivity (91.1%). User testing (n = 13) confirmed quick learning across clinicians and researchers. Application to a cohort of n = 121 pediatric cancer Trio datasets, filtering for rare protein-coding variants (MAF ≤ 0.1% in gnomAD v4.1), validated all assessable previously reported pathogenic variants. Overall, T-Rex enables clinicians to robustly analyze WES Trio data in compliance with data protection regulations without requiring additional software licenses. As one of the first platforms for comprehensive WES Trio analysis that requires no programming expertise while providing reproducible, end-to-end workflows for clinical genomics, T-Rex facilitates collaborative research between clinics and reduces reliance on external providers.

Humans↗

PubMed2026

Multi-omic analyses of the same sample using metabolomics, lipidomics, proteomics, phosphoproteomics, and glycoproteomics.

Mass spectrometry (MS)-based multi-omics offers powerful tools to comprehensively characterize proteins, post-translational modifications, metabolites, and lipids. However, these measurements are typically performed using separate sample preparation workflows and modality-specific liquid chromatography mass spectrometry (LC-MS) platforms, limiting integration and constraining applications to small amounts of sample materials, especially scarce clinical specimens. Here, we describe a unified nano-LC-MS framework that enables metabolomic, lipidomic, proteomic, phosphoproteomic, and glycoproteomic analyses from the same starting material using a single nano-LC-MS platform, with only the chromatographic conditions, acquisition methods, and enrichment procedures tailored to each omics. This integrated strategy reduces workflow complexity and sample consumption while improves analytical continuity across molecular layers. By enabling deep multi-omics characterization from the same sample, this platform provides a practical foundation for comprehensive analysis of precious clinical samples.

Proteomics↗

PubMed2026

Accelerating natural product discovery, characterization and engineering by biofoundries.

Covering: From early developments to the presentNatural product (NP) discovery is increasingly constrained by low-throughput screening, repeated rediscovery, and challenges in scaling genome mining-guided validation workflows. This highlight examines how automated biofoundries are accelerating NP discovery, characterization, and engineering through integrated design-build-test-learn (DBTL) cycles. We discuss recent advances in phenotype-first and genome-first discovery strategies enabled by robotics, high-throughput pathway reconstitution, and automated screening platforms. We further highlight emerging technologies, including cell-free biosynthesis, automated culturomics, programmable chassis engineering, and AI-assisted workflow orchestration, that may enable increasingly autonomous biofoundries for scalable exploration of NP chemical space and therapeutic discovery.

Journal Article↗

PubMed2026

Identification of a G-quadruplex-forming cell-free DNA fragment as a biomarker for the precise diagnosis of hepatocellular carcinoma.

Early detection of hepatocellular carcinoma (HCC) remains challenging, as the currently recommended surveillance strategy based on ultrasound combined with alpha-fetoprotein (AFP) is limited by suboptimal sensitivity and accessibility. Cell-free DNA (cfDNA) provides a minimally invasive avenue for cancer detection. However, most existing cfDNA-based approaches either perform unreliably in low-input samples or require analytically complex workflows. Here, we systematically profiled serum cfDNA from individuals with HCC and without HCC and identified a high-abundance tumor-associated single cfDNA fragment at the FAM230F genomic region. Integrative analysis of liver assay for transposase-accessible chromatin with sequencing (ATAC-seq) data revealed consistent tumor-specific chromatin accessibility at this locus, suggesting a tumor-derived origin. Structural characterization further demonstrated enrichment of G-quadruplex (G4) features within the target sequence, which may increase resistance to serum nuclease degradation and promote its preferential retention in circulation. Based on these properties, we established a qPCR-based detection workflow with clinical accessibility. In a validation cohort independent of the discovery cohort, a ΔC t cutoff of 2 was selected by maximizing the Youden index within the same cohort. The assay showed a sensitivity of 94.5% and a specificity of 90.5% for distinguishing HCC from non-HCC. Collectively, our study identifies FAM230F as a structurally stable tumor-associated cfDNA fragment and establishes a simple and scalable qPCR-based assay for HCC detection, providing a practical framework for translating cfDNA fragment analysis into clinical biomarkers.

Journal Article↗

PubMed2004

[A multidisciplinary mModule for oncological documentation within a hospital information system].

The follow-up documentation of oncological patients in Germany is inadequate in many cases: it is usually limited to a minimal dataset mandated by the epidemiological tumor registers; it is carried out in a paper-based fashion and rarely in a multi-disciplinary context. Parallel documentation efforts can result in redundant or erroneous data and excess work. The introduction of hospital information systems (HIS) allows the implementation of digital oncological documentation systems integrated in surrounding clinical workflows that can provide access to existing data sources as well as data entry and presentation across departmental boundaries. This concept enables the integration of tumor documentation, quality assurance and process optimization within HIS. Feasibility requirements include a high flexibility and adaptability of the underlying HIS to reach a seamless integration of oncological documentation forms within routine clinical workflows. This paper presents the conceptual design and implementation of a modular oncological documentation system at the Muenster University Hospital that is capable of integrating the documentation requirements of multiple departments within the hospital.

Computer Systems↗

PubMed2005

[Implementation of modern operating room management -- experiences made at an university hospital].

Caused by structural changes in health care the general need for cost control is evident for all hospitals. As operating room is one of the most cost-intensive sectors in a hospital, optimisation of workflow processes in this area is of particular interest for health care providers. While modern operating room management is established in several clinics yet, others are less prepared for economic challenges. Therefore, the operating room statute of the Charité university hospital useful for other hospitals to develop an own concept is presented. In addition, experiences made with implementation of new management structures are described and results obtained over the last 5 years are reported. Whereas the total number of operation procedures increased by 15 %, the operating room utilization increased more markedly in terms of time and cases. Summarizing the results, central operating room management has been proved to be an effective tool to increase the efficiency of workflow processes in the operating room.

Cost Control↗

PubMed2006

[Management of the operation room in an university hospital].

The heart of any surgical department is the operation room area. Any disturbances in the daily routine will affect the work flow of the whole hospital. As an example the major complaints of a university surgical department regarding workflow and communication are outlined. To solve these problems a team "OR organization" was established, which started the work based on a new developed OR statute. Within a short period the contentment of the employees as well as the workflow improved. But as a matter of fact, even in the following years of central OR management there is still the need to further stabilize the system and carefully improve the controlling system.

Germany↗

PubMed2006

A novel histology-directed strategy for MALDI-MS tissue profiling that improves throughput and cellular specificity in human breast cancer.

We describe a novel tissue profiling strategy that improves the cellular specificity and analysis throughput of protein profiles obtained by direct MALDI analysis. The new approach integrates the cellular specificity of histology, the accuracy and reproducibility of robotic liquid dispensing, and the speed and objectivity of automated spectra acquisition. Traditional methodologies for preparing and analyzing tissue samples rely heavily on manual procedures, which for various reasons discussed, restrict cellular specificity and sample throughput. Here, a robotic spotter deposits micron-sized droplets of matrix precisely onto foci of normal mammary epithelium, ductal carcinoma in situ, invasive mammary cancer, and peritumoral stroma selected by a pathologist from high resolution histological images of sectioned human breast cancer samples. The location of each matrix spot was then determined and uploaded into the instrument to facilitate automated profile acquisition by MALDI-TOF. In the example shown, the different lesions were clearly differentiated using mass profiling. Further, the workflow permits a visual projection of any information produced from the profile analyses directly on the histological image for a unique combination of proteomic and histological assessment of sample regions. The higher performance characteristics offered by the new workflow promises to be a significant advancement toward the next generation of tissue profiling studies.

Adult↗

PubMed2006

Challenges and opportunities in proteomics data analysis.

Accurate, consistent, and transparent data processing and analysis are integral and critical parts of proteomics workflows in general and for biomarker discovery in particular. Definition of common standards for data representation and analysis and the creation of data repositories are essential to compare, exchange, and share data within the community. Current issues in data processing, analysis, and validation are discussed together with opportunities for improving the process in the future and for defining alternative workflows.

Databases, Protein↗

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↗

PubMed2026

QPL-enabled HTSlib library: accelerating sequence file compression using Intel IAA.

SUMMARY: Sequence analysis workflows require the accessibility of large datasets, which require state-of-the-art compression tools. These compression tools, such as Samtools, often rely on HTSlib as a GZip implementation, but are still limited by throughput on time-intensive compression. QPL-HTSLib offers order of magnitude speedups for the compression and decompression of the SAM and BAM file formats commonly used in genomics workflows at the cost of a slightly larger compressed file, and is a drop-in replacement for HTSlib on Intel systems. AVAILABILITY AND IMPLEMENTATION: QPL-HTSLib is freely available on Github as an open-source software project.

Journal Article↗

PubMed2026

Fedflow: cloud orchestration for federated learning with the FeatureCloud platform.

MOTIVATION: Federated learning (FL) enables collaborative model training on geographically distributed genomic and clinical datasets while complying with data privacy laws and regulatory constraints. FeatureCloud is an existing platform for FL that provides an accessible web-based interface and a large repository of implemented methods. However, due to its graphical interface, FeatureCloud requires manual interaction of all participants, limiting automation, iteration, and reproducibility. RESULTS: We introduce fedflow, a Python-based command-line tool for headless orchestration of FL tasks with FeatureCloud. This tool uses distributed computing resources such as virtual machines or cloud instances to automate such workflows. This allows for scalable federated computing either in local simulations or deployed in a trusted environment. Further, we demonstrate how fedflow can be used to integrate FeatureCloud in reproducible Snakemake workflows. For this, we reanalyse a metagenomic dataset with two federated algorithms and compare the results to the centralized approach with pooled data. Overall, fedflow enables automation of multi-client FL tasks, facilitates embedding of FeatureCloud in standard bioinformatics pipelines and thereby helps increase reproducibility. AVAILABILITY: Fedflow is open-source and available at https://github.com/W-L/fedflow.

Journal Article↗

PubMed2025

PopGLen-a Snakemake pipeline for performing population genomic analyses using genotype likelihood-based methods.

SUMMARY: PopGLen is a Snakemake workflow for performing population genomic analyses within a genotype-likelihood framework, integrating steps for raw sequence processing of both historical and modern DNA, quality control, multiple filtering schemes, and population genomic analysis. Currently, the population genomic analyses included allow for estimating linkage disequilibrium, kinship, genetic diversity, genetic differentiation, population structure, inbreeding, and allele frequencies. Through Snakemake, it is highly scalable, and all steps of the workflow are automated, with results compiled into an HTML report. PopGLen provides an efficient, customizable, and reproducible option for analyzing population genomic datasets across a wide variety of organisms. AVAILABILITY AND IMPLEMENTATION: PopGLen is available under GPLv3 with code, documentation, and a tutorial at https://github.com/zjnolen/PopGLen. An example HTML report using the tutorial dataset is included in the Supplementary Material.

Software↗