Biomedical Discovery Research Articles

A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities.

Biomedical image analysis is fundamental for biomedical discovery. Holistic image analysis comprises interdependent subtasks such as segmentation, detection and recognition, which are tackled separately by traditional approaches. Here, we propose BiomedParse, a biomedical foundation model that can jointly conduct segmentation, detection and recognition across nine imaging modalities. This joint learning improves the accuracy for individual tasks and enables new applications such as segmenting all relevant objects in an image through a textual description. To train BiomedParse, we created a large dataset comprising over 6 million triples of image, segmentation mask and textual description by leveraging natural language labels or descriptions accompanying existing datasets. We showed that BiomedParse outperformed existing methods on image segmentation across nine imaging modalities, with larger improvement on objects with irregular shapes. We further showed that BiomedParse can simultaneously segment and label all objects in an image. In summary, BiomedParse is an all-in-one tool for biomedical image analysis on all major image modalities, paving the path for efficient and accurate image-based biomedical discovery.

NCBI RefSeq: reference sequence standards through 25years of curation and annotation.

Reference sequences and annotations serve as the foundation for many lines of research today, from organism and sequence identification to providing a core description of the genes, transcripts and proteins found in an organism's genome. Interpretation of data including transcriptomics, proteomics, sequence variation and comparative analyses based on reference gene annotations informs our understanding of gene function and possible disease mechanisms, leading to new biomedical discoveries. The Reference Sequence (RefSeq) resource created at the National Center for Biotechnology Information (NCBI) leverages both automatic processes and expert curation to create a robust set of reference sequences of genomic, transcript and protein data spanning the tree of life. RefSeq continues to refine its annotation and quality control processes and utilize better quality genomes resulting from advances in sequencing technologies as well as RNA-Seq data to produce high-quality annotated genomes, ortholog predictions across more organisms and other products that are easily accessible through multiple NCBI resources. This report summarizes the current status of the eukaryotic, prokaryotic and viral RefSeq resources, with a focus on eukaryotic annotation, the increase in taxonomic representation and the effect it will have on comparative genomics. The RefSeq resource is publicly accessible at https://www.ncbi.nlm.nih.gov/refseq.

Advanced Imaging Integration: Multi-Modal Raman Light Sheet Microscopy Combined with Zero-Shot Learning for Denoising and Super-Resolution.

This study presents an advanced integration of Multi-modal Raman Light Sheet Microscopy with zero-shot learning-based computational methods to significantly enhance the resolution and analysis of complex three-dimensional biological structures, such as 3D cell cultures and spheroids. The Multi-modal Raman Light Sheet Microscopy system incorporates Rayleigh scattering, Raman scattering, and fluorescence detection, enabling comprehensive, marker-free imaging of cellular architecture. These diverse modalities offer detailed spatial and molecular insights into cellular organization and interactions, critical for applications in biomedical research, drug discovery, and histological studies. To improve image quality without altering or introducing new biological information, we apply Zero-Shot Deconvolution Networks (ZS-DeconvNet), a deep-learning-based method that enhances resolution in an unsupervised manner. ZS-DeconvNet significantly refines image clarity and sharpness across multiple microscopy modalities without requiring large, labeled datasets, or introducing artifacts. By combining the strengths of multi-modal light sheet microscopy and ZS-DeconvNet, we achieve improved visualization of subcellular structures, offering clearer and more detailed representations of existing data. This approach holds significant potential for advancing high-resolution imaging in biomedical research and other related fields.

Emerging Therapeutic Approaches Targeting Ferroptosis in Cancer: Focus on Immunotherapy and Nanotechnology.

Ferroptosis is a newly discovered form of programmed cell death characterized by iron overload, ROS accumulation, and lipid peroxidation. It is distinguished by unique morphological, biochemical, and genetic features and stands apart from other known regulated cell death mechanisms. Studies have demonstrated a close association between ferroptosis and various cancers, including liver cancer, lung cancer, renal cell carcinoma, colorectal cancer, pancreatic cancer, and ovarian cancer. Inducing ferroptosis has shown promising results in inhibiting tumor growth and reversing tumor progression. However, the challenge lies in regulating ferroptosis in vivo due to the scarcity of potent compounds that can activate it. Integrating emerging biomedical discoveries and technological innovations with conventional therapies is imperative. Notably, considerable progress has been made in cancer treatment by leveraging immunotherapy and nanotechnology to trigger ferroptosis. This review explores the relationship between ferroptosis and emerging immunotherapies and nanotechnologies, along with their potential underlying mechanisms, offering valuable insights for developing novel cancer treatment strategies.

High-quality peptide evidence for annotating non-canonical open reading frames as human proteins.

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.

What are Proteome Atlases good for?

: The technological advances in mass spectrometry and associated computational tools have enabled the development of proteome atlases and comprehensive catalogs of proteome snapshots that have gradually transformed biomedical research. These proteome catalogs in specific biological contexts, which focused initially on model organisms, have now expanded their scope to encompass diverse organisms, tissues, and experimental conditions. These atlases, such as the Human Protein Atlas (HPA), Peptide Atlas, and Global Proteome Machine Database (GPMDB), etc. provide invaluable insights into protein expression, subcellular localization, interactions, modifications, and functions. They aid in understanding biological processes, identifying disease biomarkers, and discovering novel therapeutic targets. Despite their potential, proteome atlases face challenges like data completeness, integration with other omics data, and ethical considerations. Addressing these challenges is vital for further progress. Proteome atlases serve as indispensable resources, driving biomedical discovery and innovation.

Regulator of G-protein signaling expression in human intestinal enteroendocrine cells and potential role in satiety hormone secretion in health and obesity

Gut L-type enteroendocrine cells (EECs) are intestinal chemosensory cells that secrete satiety hormones GLP-1 and PYY in response to activation of G-protein coupled receptors (GPCRs) by luminal components of nutrient digestion and microbial fermentation. Regulator of G-protein Signaling (RGS) proteins are negative regulators of GPCR signaling. The expression profile of RGS in EECs, and their potential role in satiety hormone secretion and obesity is unknown. Transcriptomic profiling of RGS was completed in native colonic EECs was completed using single-cell RNA sequencing (scRNA-Seq) in lean and obesity, and human jejunal EECs with data obtained from a publicly available RNAseq dataset (GSE114853). RGS validation studies were completed using whole mucosal intestinal tissue obtained during endoscopy in 61 patients (n=42 OB, n=19 Lean); a subset of patients' postprandial plasma was assayed for GLP-1 and PYY. Exvivo human intestinal cultures and invitro NCI-H716cells overexpressing RGS9 were exposed to GLP-1 secretagogues in conjunction with a nonselective RGS-inhibitor and assayed for GLP-1 secretion. Transcriptomic profiling of colonic and jejunal enteroendocrine cells revealed a unique RGS expression profile in EECs, and further within GLP-1+ L-type EECs. In obesity the RGS expression profile was altered in colonic EECs. Human gut RGS9 expression correlated positively with BMI and negatively with postprandial GLP-1 and PYY. RGS inhibition in human intestinal cultures increased GLP-1 release from EECs exvivo. NCI-H716cells overexpressing RGS9 displayed defective nutrient-stimulated GLP-1 secretion. This study introduces the expression profile of RGS in human EECs, alterations in obesity, and suggests a role for RGS proteins as modulators of GLP-1 and PYY secretion from intestinal EECs. AA is supported by the NIH(C-Sig P30DK84567, K23 DK114460), a Pilot Award from the Mayo Clinic Center for Biomedical Discovery, and a Translational Product Development Fund from The Mayo Clinic Center for Clinical and Translational Science Office of Translational Practice in partnership with the University of Minnesota Clinical and Translational Science Institute.

GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console.

The vast generation of genetic data poses a significant challenge in efficiently uncovering valuable knowledge. Introducing GENEVIC, an AI-driven chat framework that tackles this challenge by bridging the gap between genetic data generation and biomedical knowledge discovery. Leveraging generative AI, notably ChatGPT, it serves as a biologist's "copilot." It automates the analysis, retrieval, and visualization of customized domain-specific genetic information, and integrates functionalities to generate protein interaction networks, enrich gene sets, and search scientific literature from PubMed, Google Scholar, and arXiv, making it a comprehensive tool for biomedical research. In its pilot phase, GENEVIC is assessed using a curated database that ranks genetic variants associated with Alzheimer's disease, schizophrenia, and cognition, based on their effect weights from the Polygenic Score (PGS) Catalog, thus enabling researchers to prioritize genetic variants in complex diseases. GENEVIC's operation is user-friendly, accessible without any specialized training, secured by Azure OpenAI's HIPAA-compliant infrastructure, and evaluated for its efficacy through real-time query testing. As a prototype, GENEVIC is set to advance genetic research, enabling informed biomedical decisions. GENEVIC is publicly accessible at https://genevicanath2024.streamlit.app. The underlying code is open-source and available via GitHub at https://github.com/bsml320/GENEVIC.git (also at https://github.com/anath2110/GENEVIC.git).

Advances in bioinformatics-based protein function prediction

With the increasing of computer power and rapid expansion of biological data, the application of bioinformatics tools has become the mainstream approach to address biological problems. The accurate identification of protein function by bioinformatics tools is crucial for both biomedical research and drug discovery, making it a hot topic of research. In this paper, we categorize bioinformatics-based protein function prediction methods into three categories: protein sequence-based methods, protein structure-based methods, and protein interaction networks-based methods. We further analyze these specific algorithms, highlighting the latest research advancements and providing valuable references for the application of bioinformatics-based protein function prediction in biomedical research and drug discovery.

Applications of CRISPR in Parasitology.

Clustered Regions of Interspersed Palindromic Repeat (CRISPR)-based techniques have been utilized in various research areas, including agriculture, biotechnology, and medicine. With the use of a short sequence guide RNA and CRISPR-associated (Cas) protein, this technique allows for robust, site-specific manipulation of the genome, aiding researchers in making important biomedical discoveries and scientific advancements. In this review, we explored the applications of CRISPR/Cas systems in the field of parasitology for the identification and validation of novel functional genes, diagnosis of parasitic infections, reduction of parasite virulence, and the disruption of disease transmission. We also discussed how CRISPR can be used for the development of therapeutics, vaccines, and drug discovery. Furthermore, the challenges and future perspectives of this technology are also highlighted.

Inclusion of Sex and Gender to Improve the State of the Science in Women's Health.

The influence of sex and gender-related factors on health and disease at all levels of scale, across all health conditions, and throughout the entire life course is increasingly clear. A series of policies instituted by the National Institutes of Health (NIH) that require researchers to include appropriate populations and to analyze the data accordingly have strengthened the evidence base around the health of women. Translating these advances to the entire research ecosystem can catalyze rigorous biomedical discovery that can improve health. We encourage journals, publishers, and funders to align their policies and expectations regarding sex and gender considerations in research with those of the NIH and other international funding agencies.

Custom Biomedical FAIR Data Analysis in the Cloud Using CAVATICA.

The historically fragmented biomedical data ecosystem has moved towards harmonization under the findable, accessible, interoperable, and reusable (FAIR) data principles, creating more opportunities for cloud-based research. This shift is especially opportune for scientists across diverse domains interested in implementing creative, nonstandard computational analytic pipelines on large and varied datasets. However, executing custom cloud analyses may present difficulties, particularly for investigators lacking advanced computational expertise. Here, we present an accessible, streamlined approach for the cloud compute platform CAVATICA that offers a solution. We outline how we developed a custom workflow in the cloud, for analyzing whole genome sequences of case-parent trios to detect sex-specific genetic effects on orofacial cleft risk, which required several programming languages and custom software packages. The approach involves just three components: Docker to containerize software environments, tool creation for each analysis step, and a visual workflow editor to weave the tools into a Common Workflow Language (CWL) pipeline. Our approach should be accessible to any investigator with basic computational skills, is readily extended to implement any scalable high-throughput biomedical data analysis in the cloud, and is applicable to other commonly used compute platforms such as BioData Catalyst. We believe our approach empowers versatile data reuse and promotes accelerated biomedical discovery in a time of substantial FAIR data.

Empowering healthcare with NLP-driven deep learning unveiling biomedical materials through text mining

This study presents a comprehensive approach to automated biomedical materials discovery in healthcare applications by integrating text mining and deep learning techniques. The research methodology encompasses two main components: exploration of key research questions through graphical representation and evaluation of model performance using precision, recall, and F1-score metrics. The identification of pertinent research questions is visualized using bar charts, offering insights into the distribution of studies across domains such as data harmonization, heterogeneity, industrial textual data, and sequential data performance. The precision comparison chart highlights the strengths and weaknesses of different models, with model 1 demonstrating notable precision. The recall comparison chart emphasizes model 2’s outstanding performance in capturing relevant information, while the f1-score comparison chart showcases the balanced metrics of model 2 and 4. These visual analyses contribute to a nuanced understanding of the research landscape and guide the development of the proposed NLP-ML pipeline. The study’s findings underscore the significance of addressing data harmonization challenges and extracting insights from industrial textual data in advancing biomedical materials discovery. Overall, this research amalgamates exploratory data analysis and quantitative model evaluation to contribute to the evolving field of text mining and deep learning applications in biomedical material discovery for healthcare applications.

A Novel Microfluidics Droplet-Based Interdigitated Ring-Shaped Electrode Sensor for Lab-on-a-Chip Applications.

This paper presents a comprehensive study focusing on the detection and characterization of droplets with volumes in the nanoliter range. Leveraging the precise control of minute liquid volumes, we introduced a novel spectroscopic on-chip microsensor equipped with integrated microfluidic channels for droplet generation, characterization, and sensing simultaneously. The microsensor, designed with interdigitated ring-shaped electrodes (IRSE) and seamlessly integrated with microfluidic channels, offers enhanced capacitance and impedance signal amplitudes, reproducibility, and reliability in droplet analysis. We were able to make analyses of droplet length in the range of 1.0-6.0 mm, velocity of 0.66-2.51 mm/s, and volume of 1.07 nL-113.46 nL. Experimental results demonstrated that the microsensor's performance is great in terms of droplet size, velocity, and length, with a significant signal amplitude of capacitance and impedance and real-time detection capabilities, thereby highlighting its potential for facilitating microcapsule reactions and enabling on-site real-time detection for chemical and biosensor analyses on-chip. This droplet-based microfluidics platform has great potential to be directly employed to promote advances in biomedical research, pharmaceuticals, drug discovery, food engineering, flow chemistry, and cosmetics.

Navigating the Nanoscale: Unraveling the Complexities of Metallic Nanoparticle Biosynthesis for Biomedical Breakthroughs and Addressing Toxicity Concerns

Navigating the Nanoscale: Unraveling the Complexities of Metallic Nanoparticle Biosynthesis for Biomedical Breakthroughs and Addressing Toxicity Concerns

A guide to successful management of collaborative partnerships in quantitative research: An illustration of the science of team science.

Data-intensive research continues to expand with the goal of improving healthcare delivery, clinical decision-making, and patient outcomes. Quantitative scientists, such as biostatisticians, epidemiologists, and informaticists, are tasked with turning data into health knowledge. In academic health centres, quantitative scientists are critical to the missions of biomedical discovery and improvement of health. Many academic health centres have developed centralized Quantitative Science Units which foster dual goals of professional development of quantitative scientists and producing high quality, reproducible domain research. Such units then develop teams of quantitative scientists who can collaborate with researchers. However, existing literature does not provide guidance on how such teams are formed or how to manage and sustain them. Leaders of Quantitative Science Units across six institutions formed a working group to examine common practices and tools that can serve as best practices for Quantitative Science Units that wish to achieve these dual goals through building long-term partnerships with researchers. The results of this working group are presented to provide tools and guidance for Quantitative Science Units challenged with developing, managing, and evaluating Quantitative Science Teams. This guidance aims to help Quantitative Science Units effectively participate in and enhance the research that is conducted throughout the academic health centre-shaping their resources to fit evolving research needs.

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. Cryo2StructData is larger than existing, publicly available datasets for training AI methods to build atomic protein structures from cryo-EM density maps. We trained and tested deep learning models on Cryo2StructData to validate its quality showing that it is ready for being used to train and test AI methods for building atomic models.

Effect of Endothelin-1 on Resting Blood Pressure and the Blood Pressure Response to Hypoxia in Healthy Young Men

Objectives: Upregulation of endothelin-1 (ET-1) and its receptors have been linked to increases in hypoxic sensitivity of the carotid chemoreceptors and the development of hypertension. As such, chemoreflex sensitization has been shown in pre-clinical models to be attenuated by ET-1 receptor blockade. Herein, we sought to assess a role for ET-1 in the maintenance of resting blood pressure (BP) in healthy young men and determine whether chemoreflex control of BP could be altered by ET-1 blockade. We hypothesized endothelin receptor antagonism with oral bosentan would lower resting BP in healthy young men as well as the acute BP response to hypoxia. Methods: Twenty-four healthy young men (31±5 yrs, 26±3 kg/m2) completed two study visits (control, bosentan) separated by a minimum of 1 week. The nonspecific endothelin receptor antagonist bosentan (62.5 mg) was taken by mouth twice daily for 3 days prior to the second study visit. On each visit, beat-by-beat BP was assessed under 3 inspiratory air conditions: 1) steady-state normoxia (FiO2 0.21), 2) chemoreflex excitation produced by acute, graded hypoxia (FiO2 0.05 – 0.21), 3) chemoreflex inhibition elicited by transient hyperoxia (FiO2 1.00). Results: Oral bosentan treatment resulted in an increase in plasma ET-1 (0.9±0.9 to 1.3±0.6 pg/mL, p=0.004), supporting receptor blockade. Resting diastolic and mean BP were reduced following 3 days of bosentan treatment compared to control (diastolic: 73±5 to 69±7 mmHg, p=0.007; mean: 93±7 to 88±7 mmHg, p=0.005) with no change in systolic BP (p=0.507). Notably, the BP response to both acute hypoxia (-0.48±0.38 to -0.25±0.31 mmHg/%, p=0.004) and hyperoxia (main effect of bosentan, p=0.025) were attenuated following bosentan. Conclusions: Acute oral bosentan treatment resulted in a reduction in resting BP in healthy young men. The effect of endothelin receptor inhibition with bosentan further attenuated the rise in BP during chemoreflex excitation via acute, graded hypoxia as well as the fall in BP during chemoreflex inhibition (transient hyperoxia). Together these data support a role for ET-1 in control of resting BP in healthy young men, possibly through a chemoreceptor-mediated mechanism. NIH HL130339 (JL), Mayo Clinic Center for Biomedical Discovery (JL, SB). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

A complement C4-derived glycopeptide is a biomarker for PMM2-CDG.

BACKGROUNDDiagnosis of PMM2-CDG, the most common congenital disorder of glycosylation (CDG), relies on measuring carbohydrate-deficient transferrin (CDT) and genetic testing. CDT tests have false negatives and may normalize with age. Site-specific changes in protein N-glycosylation have not been reported in sera in PMM2-CDG.METHODSUsing multistep mass spectrometry-based N-glycoproteomics, we analyzed sera from 72 individuals to discover and validate glycopeptide alterations. We performed comprehensive tandem mass tag-based discovery experiments in well-characterized patients and controls. Next, we developed a method for rapid profiling of additional samples. Finally, targeted mass spectrometry was used for validation in an independent set of samples in a blinded fashion.RESULTSOf the 3,342 N-glycopeptides identified, patients exhibited decrease in complex-type N-glycans and increase in truncated, mannose-rich, and hybrid species. We identified a glycopeptide from complement C4 carrying the glycan Man5GlcNAc2, which was not detected in controls, in 5 patients with normal CDT results, including 1 after liver transplant and 2 with a known genetic variant associated with mild disease, indicating greater sensitivity than CDT. It was detected by targeted analysis in 2 individuals with variants of uncertain significance in PMM2.CONCLUSIONComplement C4-derived Man5GlcNAc2 glycopeptide could be a biomarker for accurate diagnosis and therapeutic monitoring of patients with PMM2-CDG and other CDGs.FUNDINGU54NS115198 (Frontiers in Congenital Disorders of Glycosylation: NINDS; NCATS; Eunice Kennedy Shriver NICHD; Rare Disorders Consortium Disease Network); K08NS118119 (NINDS); Minnesota Partnership for Biotechnology and Medical Genomics; Rocket Fund; R01DK099551 (NIDDK); Mayo Clinic DERIVE Office; Mayo Clinic Center for Biomedical Discovery; IA/CRC/20/1/600002 (Center for Rare Disease Diagnosis, Research and Training; DBT/Wellcome Trust India Alliance).

The 'bIUreactor': An Open-Source 3D Tissue Research Platform.

We developed the open-source bIUreactor research platform for studying 3D structured tissues. The versatile and modular platform allows a researcher to generate 3D tissues, culture them with oxygenated perfusion, and provide cyclic loading, all in their own lab (in laboratorium) for an all in cost of $8,000 including 3D printer, printing resin, and electronics. We achieved this by applying a design philosophy that leverages 3D printing, open-source software and hardware, and practical techniques to produce the following: 1. perfusible 3D tissues, 2. a bioreactor chamber for tissue culture, 3. a module for applying cyclic compression, 4. a peristaltic pump for providing oxygenated perfusion to 3D tissues, 5. motor control units, and 6. open-source code for running the control units. By making it widely available for researchers to investigate 3D tissue models and easy for them to use, we intend for the bIUreactor to democratize 3D tissue research, therefore increasing the pace and scale of biomedical research discoveries using 3D tissue models.