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Moving Beyond Morphology to Multiplexed Molecular Imaging as the Next Frontier in Diagnostic Pathology.

Diagnostic pathology has long relied on the morphologic interpretation of hematoxylin and eosin-stained tissues to guide diagnosis and assess prognostic features. Although pathologists intuitively recognize spatial patterns and architectural organization, these assessments remain largely qualitative and difficult to quantify systematically. Immunohistochemistry and immunofluorescence have introduced molecular specificity but are limited in multiplexing capacity, whereas bulk genomic and transcriptomic assays provide high molecular depth but lose spatial context by averaging signals across heterogeneous cell populations. Recent advances in spatial proteomics-including mass spectrometry-based imaging and cyclic immunofluorescence-now enable multiplexed, single-cell protein analysis within intact tissue architecture. These technologies have revealed complex immune and stromal microenvironments, spatially organized biomarkers predictive of therapeutic response, and molecular gradients underlying disease progression. By integrating histologic and molecular information, spatial proteomics bridges traditional microscopy with high-dimensional omics, allowing quantitative, spatially resolved insights into tissue organization and disease mechanisms. This review summarizes recent developments in multiplexed spatial proteomics from both scientific and pathologic perspectives, highlighting how these technologies extend beyond morphology to quantify histologic patterns, refine biomarker discovery, and facilitate clinical translation. The review also examines translational challenges and barriers to clinical implementation, including costs, standardization requirements, and workflow integration.

Humans↗

Operational conditions affecting the vaccination of older adults.

BACKGROUND: The content and context of the process of vaccinating older adults against influenza in outpatient settings has not been adequately described. Failure to appreciate the causal antecedents or precursors to the act of provider recommendation may explain why so many efficacious interventions identified by the U.S. Task Force on Community Preventive Services fail to be routinely implemented and why influenza immunization rates have remained static over the past decade. METHODS: This study used critical path analysis from data collected during standardized workflow observations of patients more than 50 years of age from a convenience sample of 16 ambulatory care settings in San Diego, California; Rochester, New York; and Albuquerque, New Mexico. Observations were made from October 23, 2001 to January 31, 2002. RESULTS: In this study, 62% (151/243) of patients observed during scheduled extended visits received influenza vaccinations. When operational, temporal, and clinical factors are examined altogether through critical path analysis, a model of seven critical organizational support, temporal, and clinical activities emerges that is able to predict 93% of the immunizations. Variation from the model predicts 73% of the missed opportunities. CONCLUSIONS: Vaccination of adults should not be seen as simply an incremental activity added to the general health encounter. Assuring a high rate of vaccination requires adequate time and operational support. Provider-patient discussion is more productively viewed as the culmination of the immunization process, not the beginning. Finally, this study indicates the potential need to identify and compare processes of care associated with other specific preventive services.

Aged↗

An emergency department response to severe acute respiratory syndrome: a prototype response to bioterrorism.

STUDY OBJECTIVE: On March 13, 2003, Singapore physicians were alerted about an outbreak of atypical pneumonia that became known as severe acute respiratory syndrome (SARS). I describe the application of an emergency department (ED) disaster response plan to manage the SARS outbreak. METHODS: The ED implemented protection for staff, patients, and facility; infection control measures; and disaster-response workflow changes. The Ministry of Health, Singapore, centralized SARS cases in the hospital, and the ED became the national screening center. A screening questionnaire and a set of admission criteria were applied after assessment of clinical features and chest radiograph findings. RESULTS: For the duration of the outbreak that ended on May 31, 2003, the ED screened 11,461 persons for SARS, of whom 1,386 (12.9%) were admitted to rule out SARS and 235 (17%) were confirmed to have SARS. Among 10,075 persons discharged from the ED, there were 28 reattending patients who were admitted and diagnosed with SARS, giving an undertriage rate of 0.3% (95% confidence interval [CI] 0.1% to 0.4%). The sensitivity of an ED admission for SARS was 89.4% (95% CI 85.6% to 93.1%), and specificity was 89.7% (95% CI 89.2% to 90.3%). The positive predictive value was 17% (95% CI 15.7% to 18.4%), and the negative predictive value was 99.7% (95% CI 99.6% to 99.8%). No patient contracted SARS as a result of an ED visit. After full implementation of protective measures, 1 ED nurse with undiagnosed diabetes mellitus was treated for suspected SARS. CONCLUSION: Although the SARS outbreak was not a bioterrorism event, the ED disaster response was applicable in the outbreak's management. The use of a screening questionnaire and admission criteria enabled the ED to screen, treat, and safely discharge the majority of the patients.

Adult↗

Application of emerging technologies in the antiviral field.

Viral diseases pose a serious threat to global public health, agriculture, and biosecurity. Conventional antiviral strategies are often limited by an incomplete understanding of disease mechanisms, poor targeting precision, and slow response times. Emerging technologies are now reshaping the landscape of antiviral research. This review examines the roles of four key frontiers, including organoid models, gene editing, AI-driven molecular design, and synthetic biology. Organoids provide physiologically relevant platforms that model virus-host interactions and disease progression. Viral infections remain a major challenge to human and animal health, agriculture, and biosecurity. Progress in antiviral research is constrained by the complexity of viral pathogenesis, the diversity and rapid evolution of viruses, and the limited translational relevance of some traditional model systems. Recent advances in organoid technology, gene editing, artificial intelligence, and synthetic biology are expanding the toolkit available for antiviral research and development. In this review, we discuss how these four technological frontiers contribute to disease modeling, target discovery, molecular design, and translational innovation. Organoids, in particular, provide physiologically relevant systems for investigating viral infection, tissue tropism, host responses, and pathogenesis. Gene editing tools, such as CRISPR, enable precise manipulation of host and viral genomes, facilitating the development of resistant organisms and next-generation vaccine platforms. AI technologies, including AlphaFold for structure prediction and platforms for de novo protein design, address long-standing bottlenecks in structural biology and offer powerful means to engineer antiviral proteins, antibodies, and vaccine antigens. Synthetic biology, guided by the Design-Build-Test-Learn cycle, integrates computational design, genetic assembly, and functional validation into a cohesive pipeline. Together, these technologies form a synergistic workflow that spans disease modeling, target discovery, molecular design, construction, testing, and iterative optimization. This integrated approach is shifting antiviral development from traditional empirical methods toward more precise, intelligent strategies. The review also highlights ongoing challenges in integration and scalability, stressing that high-quality biological datasets and stronger interdisciplinary collaboration are essential for realizing translational potential. By presenting a cohesive view of these converging methodologies, this review offers a framework to guide the intelligent evolution of antiviral strategies in both human and animal health.

Antiviral↗

Approaches for creating computer-interpretable guidelines that facilitate decision support.

During the last decade, studies have shown the benefits of using clinical guidelines in the practice of medicine. Although the importance of these guidelines is widely recognized, health care organizations typically pay more attention to guideline development than to guideline implementation for routine use in daily care. However, studies have shown that clinicians are often not familiar with written guidelines and do not apply them appropriately during the actual care process. Implementing guidelines in computer-based decision support systems promises to improve the acceptance and application of guidelines in daily practice because the actions and observations of health care workers are monitored and advice is generated whenever a guideline is not followed. Such implementations are increasingly applied in diverse areas such as policy development, utilization management, education, clinical trials, and workflow facilitation. Many parties are developing computer-based guidelines as well as decision support systems that incorporate these guidelines. This paper reviews generic approaches for developing and implementing computer-based guidelines that facilitate decision support. It addresses guideline representation, acquisition, verification and execution aspects. The paper describes five approaches (the Arden Syntax, GuideLine Interchange Format (GLIF), PROforma, Asbru and EON), after the approaches are compared and discussed.

Artificial Intelligence↗

A cognitive blueprint of collaboration in context: distributed cognition in the psychiatric emergency department.

OBJECTIVE: The complex cognitive processes that underlie human performance in 'messy' contexts such as critical care medicine suggest a need for a cognitive model with broad scope to support the understanding of error in such domains. The objective of this research is to characterize the cognition that underlies patient care in the domain of emergency psychiatry in order to enhance the understanding of error in this context. METHODS AND MATERIALS: The theoretical framework of distributed cognition has been used to study collaborative decision-making in a number of similarly complex environments such as airline cockpits and air traffic control towers. These environments share certain characteristics with the critical care domain: the work is collaborative in nature, it is supported by artifacts that can be studied directly, and the consequences of error are dire. However, the nature of the work in this domain and the artifacts used to support it are unique. The application of the theoretical constructs of distributed cognition to this context is necessary in order to characterize the collective thinking that underlies critical care. Our research uses a combination of ethnographic and interview data to derive a distributed cognitive model of the psychiatric emergency department (PED), a high volume clinical unit dealing exclusively with the acute phases of psychiatric crises. The dynamics of workflow within the department are complex: several types of clinician collaborate by forming temporary multidisciplinary teams that attach to and manage particular patients. The component members of these teams change over time. RESULTS: Using the theoretical framework of distributed cognition, we interpreted the collected data to derive a cognitive model of the distribution of work and information flow in the PED. This modeling process has revealed several latent flaws in the system related to the underlying distribution of cognition across teams, time, space and artifacts. CONCLUSIONS: The characterization of this distribution has enhanced our understanding of the cognitive dynamics underlying error in this environment, and will serve to guide future research on error management in the ED and inform the development of context-appropriate error-management systems.

Artificial Intelligence↗

Deep generative models in biological sequence and structure analysis and design.

Deep generative models have transformed biological sequence modeling from predictive analysis toward increasingly controllable design. Early biological applications of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) established latent representation learning and sequence synthesis, while recent advances in transformer-based language models, discrete diffusion, flow-matching, and multimodal generative frameworks have substantially expanded the scope of biological design. This review examines generative models for DNA, RNA, and protein sequence design, emphasizing how different model classes represent biological constraints, operate over discrete and continuous spaces, and integrate sequence, structure, and function. We compare VAEs, GANs, autoregressive and masked language models, diffusion models, and flow-based approaches across genomics, transcriptomics, and proteomics, with particular attention to controllability, long-range dependency modeling, structural grounding, generalization, and experimental utility. We further examine evaluation strategies, out-of-distribution generalization, and closed-loop design-build-test-learn workflows that connect in silico generation with empirical validation. We distinguish fundamental modality-dependent constraints including sequence discreteness, context length, structural coupling, and physical or thermodynamic requirements from architecture-dependent advantages that reflect the current state of the field. Current studies suggest that long-context models are particularly useful for genome-scale representation and sequence modeling, whereas structure-aware diffusion, flow-based, and inverse-folding approaches provide better frameworks for geometry-constrained RNA and protein design. This perspective provides a critical framework for understanding the present capabilities, limitations, and convergence of generative approaches toward reliable and experimentally grounded biological design.

Biological sequence analysis↗

Proteome-wide structural and interaction analysis using cross-linking mass spectrometry and its applications.

Deciphering the mechanisms of protein-protein interactions (PPIs) and protein structural changes within the native cellular environment is crucial for advancing drug discovery. In vivo chemical cross-linking coupled with mass spectrometry (XL-MS) captures weak, transient, and higher-order interactions that are often dysregulated under altered physiological conditions and remain challenging to detect using conventional methods. Applications of in vivo XL-MS range from targeted mapping of PPIs to large-scale identification of interactome networks within the cells. The integration of quantitative approaches further facilitates comparison across different physiological conditions. The recent incorporation of machine learning (ML) tools into XL-MS workflows is transforming the depth and efficiency of this technology. AI-driven algorithms now enable more accurate identification of cross-linked peptides and the mapping of interaction topologies. Furthermore, the synergistic coupling of in vivo XL-MS data with AI-assisted structural modeling platforms such as AlphaFold allows dynamic and high-throughput prediction of protein networks. This review discusses the broader applications of in vivo XL-MS in complex biological samples, ranging from organelles and cells to whole tissues, and highlights how AI integration is expanding structural biology toward a systems-level understanding of proteome architecture.

Mass Spectrometry↗

[Megavoltage cone-beam CT: Recent developments and clinical applications].

The Megavoltage cone-beam (MV CBCT) system consists of a new a-Si flat panel adapted for MV imaging and an integrated workflow application allowing the automatic acquisition of projection images, cone-beam CT image reconstruction, CT to CBCT image registration and couch position adjustment. This provides a 3D patient anatomy volume in the actual treatment position, relative to the treatment isocenter, moments before the dose delivery, that can be tightly aligned to the planning CT, allowing verification and correction of the patient position, detection of anatomical changes and dose calculation. In this paper, we present the main advantages and performance of this MV CBCT system and summarize the different clinical applications. Examples of the image-guided treatment process from the acquisition of the MV CBCT scan to the correction of the couch position and dose delivery will be presented for spinal and lung lesions and for head and neck, and prostate cancers.

Head and Neck Neoplasms↗

Integrating virtual screening in lead discovery.

Target- and ligand-based virtual screening have emerged as resource-saving techniques that have been successfully applied to identify novel chemotypes in biologically active molecules. Eight confirmed virtual screening hits have recently been described and are discussed in this review, with focus on the workflow. These are then evaluated in the light of pharmacokinetics prediction (e.g. Caco-2 permeability, cytochrome P450 inhibition and hERG binding). We anticipate problems for five of these hits (e.g. cardiac toxicity), which warrant further experiments. Future challenges include dynamic tautomer/protonation treatment for both ligands and targets and improved pre- and post- virtual screening filters.

Animals↗

Structure-resolved virus-host interactomics by cross-linking mass spectrometry.

Viruses depend on host protein networks to replicate, assemble progeny, and spread between cells and organisms. Defining these virus-host protein interactions is challenging because they are highly dependent on infection stage, cell type, species, and because mechanistic interpretation requires information about structural interfaces and conformational states. Cross-linking mass spectrometry (XL-MS) addresses these challenges by adding a spatial and structural dimension to virus-host interactomics in native systems. In this review, we discuss how XL-MS has advanced from targeted analysis of viral protein complexes to structure-resolved mapping of virion architecture and infected-cell virus-host interactomes. We highlight how XL-MS complements AP-MS, cryo-EM/cryo-ET, quantitative proteomics, genetic perturbation, and structure prediction to connect physical proximity with molecular mechanisms. Finally, we discuss current limitations in sensitivity, chemical coverage, temporal resolution, and model interpretation, and outline how future quantitative and integrative XL-MS workflows may enable systems-level structural virology.

Mass Spectrometry↗

Proteomics: the next revolution in laboratory medicine?

BACKGROUND: The identification of specific genetic alterations and protein profiles associated with disease offers a unique opportunity to develop proteomics-based assays for early diagnosis. By identifying proteins in serum/plasma, a minimally invasive tool is used to assess the presence of disease and to monitor response to treatment and/or disease progression. The potential clinical applications of this tool are broad-based, including the diagnosis not only of cancer but also cardiovascular and neuromuscular diseases, organ transplantation associated conditions, and infertility. METHODS: A number of competing chromatographic techniques have been proposed for overcoming the complexity and labor-intensive manipulations associated with the traditional technique for proteomic analysis, which is based on two-dimensional gel electrophoretic techniques. However, mass spectrometry has now assumed a central role in most proteomic workflows, and several combinations of ionization sources, analyzers and fragmentations devices have been described and developed. RESULTS: Thanks to proteomic applications in the diagnosis of cancer, several research groups have identified proteomic patterns associated with ovarian, prostatic, colorectal and other cancers. While the sensitivity and specificity of these patterns are highly satisfactory, there are still some open questions concerning the standardization, reproducibility, and inter-laboratory agreement of these data. CONCLUSIONS: Proteomics, and, in particular, serum mass spectroscopic proteomic pattern diagnostics, is a rapid expanding field of research. The plasma proteoma has an important position at the intersection between genes and diseases, and clinical laboratories must adapt to a new era of tests based on proteomics and genomics. In the future, mass spectrometry will become an essential tool in the clinical laboratory.

Biomarkers, Tumor↗

Single-cell mapping of regulatory DNA-protein interactions.

Gene expression is controlled by transcription factors (TFs), whose genome binding is shaped by chromatin accessibility and histone modifications, yet mapping these interactions, particularly those with weak affinity or a transient nature, in single cells remains technically challenging. To address this gap, we developed docking and deamination followed by sequencing (D&D-seq), a single-cell immuno-tethering technology for profiling DNA-protein interactions. D&D-seq couples an antibody-binding nanobody to a cytosine base editor, a combination that enables detection of weak or transient factor binding through targeted cytosine-to-uracil editing at protein-bound genomic sites. This approach is compatible with standard single-cell multi-omic workflows and therefore allows integrated analyses of gene regulation. Using assay for transposase-accessible chromatin using sequencing (ATAC-seq) and single-cell ATAC-seq (scATAC-seq), we assessed chromatin accessibility as a functional readout of TF activity, and by coupling D&D-seq with whole-genome sequencing, we captured CTCF binding in both active and inactive chromatin compartments.

Animals↗

Integrative proximal-ubiquitomics profiling for deubiquitinase substrate discovery applied to USP30.

The growing interest in deubiquitinases (DUBs) as drug targets for modulating critical molecular pathways in disease is fueled by the discovery of their specific cellular roles. A crucial aspect of this fact is the identification of DUB substrates. While mass spectrometry-based proteomic methods can be used to study global changes in cellular ubiquitination following DUB activity perturbation, these datasets often include indirect and downstream ubiquitination events. To enrich for the direct substrates of DUB enzymes, we have developed a proximal-ubiquitome workflow that combines proximity labeling methodology (ascorbate peroxidase-2 [APEX2]) with subsequent ubiquitination enrichment based on the K-ε-GG motif. We applied this technology to identify altered ubiquitination events in the vicinity of the DUB ubiquitin-specific protease 30 (USP30) upon its inhibition. Our findings reveal ubiquitination events previously associated with USP30 on TOMM20 and FKBP8, as well as the candidate substrate LETM1, which is deubiquitinated in a USP30-dependent manner.

Humans↗

Charting the development and engineering of CRISPR base editors: lessons and inspirations.

CRISPR base editors (BEs) have introduced a new chapter in precise genome editing. The brief but fruitful history of BE development documents many case studies that not only lay the foundation of base-editing technology but are also instrumental to future protein engineering efforts. In this review, we summarize the development and engineering of various BEs with a focus on recent progress. These include traditional cytosine and adenine base editors (CBEs and ABEs), novel TadA-derived CBEs, transversion BEs, dual BEs, and CRISPR-free BEs. We discuss each aspect of the workflow and highlight the successes and challenges encountered in the engineering process.

Gene Editing↗

Evaluation of multidimensional (ion-exchange/reversed-phase) protein separations using linear and step gradients in the first dimension.

The performance characteristics of multidimensional liquid chromatographic protein separations were evaluated using on-line electrospray mass detection, and a novel workflow for automated LC/MS data processing. Two-dimensional ion exchange/reversed-phase LC separations of Escherichia coli cytosol were conducted using either a continuous linear or discontinuous step gradient in the first dimension. Chromatographic profiles of the top 100 most abundant components were characterized to assess overall separation reproducibility within each mode, and to characterize differences in component distribution between the two modes of operation. Analysis of the resulting data indicates that multidimensional separations of complex protein mixtures can be done reproducibly. Furthermore, under the conditions employed within this study, a linear first dimension gradient was more effective at fractionating the protein mixture, distributing fewer major components to multiple second dimension cycles than an equivalent step gradient. The application of on line mass spectrometry, and automated processing of the resulting data, proved valuable for producing component level analysis of multidimensional protein separations.

Chromatography, Ion Exchange↗

Comprehensive identification of carboxylic acids by using bromine isotope-based chemical isotope labelling and structure-guided molecular network.

Carboxylic acids (CAs) are important contributors to the flavor quality of sauce-flavor Chinese Baijiu, yet their comprehensive analysis remains challenging due to poor ionization efficiency, weak chromatographic retention, and limited annotation capability. Herein, we developed a workflow for the high-coverage discovery and annotation of CAs in Baijiu by coupling chemical isotope labeling-liquid chromatography-mass spectrometry with a structure-guided molecular network strategy (SGMNS). A bromine-containing derivatization reagent, 1-(3-aminopropyl)-3-bromoquinolin-1-ium bromide (APBQ), was designed and synthesized to exploit the natural isotope distribution of bromine and characteristic MS/MS fragmentation behavior. Following APBQ derivatization, the target CAs showed superior chromatographic retention and favorable analytical performance. Based on isotopic peak pairing in MS1 and diagnostic fragment validation in MS2, 372 potential CA derivatives were discovered from pooled Baijiu samples and 355 of them were validated by diagnostic fragments in MS2 spectra. To address the scarcity of derivatized spectral libraries, SGMNS was employed for annotation using a background network constructed from APBQ-labeled candidates derived from the Expanded Chinese Baijiu Compound Database. The developed method was further applied to profile Baijiu samples, revealing pronounced differences in CA composition across the seven fermentation rounds. Notably, rounds 3 to 5 exhibited the largest numbers of differential CAs. This study provided an effective analytical strategy for large-scale CA profiling, offering new insight into the chemical basis of flavor formation during multi-round fermentation of sauce-flavor Baijiu.

Isotope Labeling↗

Peptide ligands targeting GP64 for the purification of Baculovirus from insect cell lysates.

Autographa californica multiple nucleopolyhedrovirus, known as Baculovirus, is a widely used platform for producing therapeutic proteins and viral vectors. The purity and infectious activity of Baculovirus stocks determine the quality and productivity of recombinant products produced through this system. Current purification strategies suffer from major limitations: centrifugation lacks productivity and scalability; ion-exchange chromatography affords limited selectivity and purity; and the only commercial affinity resin requires harsh elution conditions that significantly reduce functional product recovery. To overcome these limitations, this study introduces the first peptide affinity ligands targeting the baculoviral envelope glycoprotein GP64 for the purification of active Baculovirus particles. We implemented a combinatorial selection workflow based on dual-fluorescence screening of solid-phase peptide libraries to identify 12-mer sequences that bind GP64 and elute Baculovirus under mild conditions (pH 8.5). As the selected ligands are enriched in histidine and tyrosine residues, product release is effected by the combined modulation of pH and ionic strength. Eight candidate peptides (SB1-SB8) were evaluated on Toyopearl and POROS chromatographic resins, demonstrating that matrix chemistry, pore size, and ligand density govern purification performance. The lead peptide SB4 conjugated to POROS resin at ∼10 µmol/mL achieved 81% recovery of infectious virions (transducing units), robust host cell protein reduction (LRV 1.65), and a dynamic binding capacity (DBC10%) of 1.9 × 1010 vg/mL resin. Transmission electron microscopy and multi-angle light scattering confirmed the integrity of purified particles (200 × 50 nm rods with intact nucleocapsids), compared to BacuClear eluates that showed collapsed morphology. The SB4-POROS resin demonstrated storage stability and ∼80% retention of binding capacity over ten purification-regeneration cycles with caustic cleaning. Integration into a three-step downstream process (clarification, affinity capture, and polishing) raised product purity 1,528-fold, from 6.22 × 106 to 9.50 × 109 viral genomes per µg of HCP, while reducing the total HCP burden 1,698-fold, at a cumulative transducing-unit yield of ∼69% relative to the feedstock, establishing SB4-POROS as a promising technology with a favorable projected cost structure for Baculovirus purification.

Affinity chromatography↗