Biomedical Image Processing with Containers and Deep Learning: An Automated Analysis Pipeline: Data architecture, artificial intelligence, automated processing, containerization, and clusters orchestration ease the transition from data acquisition to insights in medium-to-large datasets

Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data

Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled Enabling Global Image Data Sharing in the Life Sciences, which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data. In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.

A Modular Framework for Data Processing at the Edge: Design and Implementation

There is a rapid increase in the number of edge devices in IoT solutions, generating vast amounts of data that need to be processed and analyzed efficiently. Traditional cloud-based architectures can face latency, bandwidth, and privacy challenges when dealing with this data flood. There is currently no unified approach to the creation of edge computing solutions. This work addresses this problem by exploring containerization for data processing solutions at the network's edge. The current approach involves creating a specialized application compatible with the device used. Another approach involves using containerization for deployment and monitoring. The heterogeneity of edge environments would greatly benefit from a universal modular platform. Our proposed edge computing-based framework implements a streaming extract, transform, and load pipeline for data processing and analysis using ZeroMQ as the communication backbone and containerization for scalable deployment. Results demonstrate the effectiveness of the proposed framework, making it suitable for time-sensitive IoT applications.

Translational biophotonics with Raman imaging: clinical applications and beyond

Clinical medicine continues to seek novel rapid non-invasive tools capable of providing greater insight into disease progression and management. Raman scattering based technologies constitute a set of tools under continuing development to address outstanding challenges spanning diagnostic medicine, surgical guidance, therapeutic monitoring, and histopathology. Here we review the mechanisms and clinical applications of Raman scattering, specifically focusing on high-speed imaging methods that can provide spatial context for translational biomedical applications.

Recent progress in plasmonic nanoparticle-based biomarker detection and cytometry for the study of central nervous system disorders

Neurological disorders affect hundreds of millions of people around the world, are often life-threatening, untreatable, and can result in debilitating symptoms. The high prevalence of these disorders, which feature biochemical or structural abnormalities in neuronal systems, has spurned innovations in both rapid and early detection to assist in the selection of appropriate treatment strategies to improve the patients' quality of life. Plasmonic nanoparticles (PNPs), a versatile and promising class of nanomaterials, are widely utilized in numerous imaging techniques, drug delivery systems, and biomarker detection methods. Recently, PNP-based nanoprobes have attracted considerable attention for the early diagnosis of neurological disorders. Gold nanoparticles (AuNPs), with high local surface plasmon resonance (LSPR) signals, have been particularly well exploited as probes for dynamic biomarker detection, with quantification sensitivity demonstrated down to the single-molecule level. In this review, we will discuss the possibilities of PNPs in the methodological development for rapid neurological disease identification. In addition, we will also describe a new digital cytometry method that combines dark-field imaging and machine learning for precise biomarker enumeration on single cells. The aim of this review is to attract researchers working on the future development of new plasmonic nanoprobe-based strategies for the diagnosis of neurological disorders.

Characterization of Cell-Bound CA125 on Immune Cell Subtypes of Ovarian Cancer Patients Using a Novel Imaging Platform

MUC16, a sialomucin that contains the ovarian cancer biomarker CA125, binds at low abundance to leucocytes via the immune receptor, Siglec-9. Conventional fluorescence-based imaging techniques lack the sensitivity to assess this low-abundance event, prompting us to develop a novel "digital" optical cytometry technique for qualitative and quantitative assessment of CA125 binding to peripheral blood mononuclear cells (PBMC). Plasmonic nanoparticle labeled detection antibody allows assessment of CA125 at the near-single molecule level when bound to specific immune cell lineages that are simultaneously identified using multiparameter fluorescence imaging. Image analysis and deep learning were used to quantify CA125 per each cell lineage. PBMC from treatment naïve ovarian cancer patients (N = 14) showed higher cell surface abundance of CA125 on the aggregate PBMC population as well as on NK (p = 0.013), T (p < 0.001) and B cells (p = 0.024) compared to circulating lymphocytes of healthy donors (N = 7). Differences in CA125 binding to monocytes or NK-T cells between the two cohorts were not significant. There was no correlation between the PBMC-bound and serum levels of CA125, suggesting that these two compartments are not in stoichiometric equilibrium. Understanding where and how subset-specific cell-bound surface CA125 takes place may provide guidance towards a new diagnostic biomarker in ovarian cancer.

Density Distribution Maps: A Novel Tool for Subcellular Distribution Analysis and Quantitative Biomedical Imaging

Subcellular spatial location is an essential descriptor of molecules biological function. Presently, super-resolution microscopy techniques enable quantification of subcellular objects distribution in fluorescence images, but they rely on instrumentation, tools and expertise not constituting a default for most of laboratories. We propose a method that allows resolving subcellular structures location by reinforcing each single pixel position with the information from surroundings. Although designed for entry-level laboratory equipment with common resolution powers, our method is independent from imaging device resolution, and thus can benefit also super-resolution microscopy. The approach permits to generate density distribution maps (DDMs) informative of both objects’ absolute location and self-relative displacement, thus practically reducing location uncertainty and increasing the accuracy of signal mapping. This work proves the capability of the DDMs to: (a) improve the informativeness of spatial distributions; (b) empower subcellular molecules distributions analysis; (c) extend their applicability beyond mere spatial object mapping. Finally, the possibility of enhancing or even disclosing latent distributions can concretely speed-up routine, large-scale and follow-up experiments, besides representing a benefit for all spatial distribution studies, independently of the image acquisition resolution. DDMaker, a Software endowed with a user-friendly Graphical User Interface (GUI), is also provided to support users in DDMs creation.

Bird Eye View of Protein Subcellular Localization Prediction

Proteins are made up of long chain of amino acids that perform a variety of functions in different organisms. The activity of the proteins is determined by the nucleotide sequence of their genes and by its 3D structure. In addition, it is essential for proteins to be destined to their specific locations or compartments to perform their structure and functions. The challenge of computational prediction of subcellular localization of proteins is addressed in various in silico methods. In this review, we reviewed the progress in this field and offered a bird eye view consisting of a comprehensive listing of tools, types of input features explored, machine learning approaches employed, and evaluation matrices applied. We hope the review will be useful for the researchers working in the field of protein localization predictions.

Plasmonic Nanoparticle-Based Digital Cytometry to Quantify MUC16 Binding on the Surface of Leukocytes in Ovarian Cancer

Although levels of the circulating ovarian cancer marker (CA125) can distinguish ovarian masses that are likely to be malignant and correlate with severity of disease, serum CA125 has not proved useful in general population screening. Recently, cell culture studies have indicated that MUC16 may bind to the Siglec-9 receptor on natural killer (NK) cells where it downregulates the cytotoxicity of NK cells, allowing ovarian cancer cells to evade immune surveillance. We present evidence that the presence of MUC16 can be locally visualized and imaged on the surface of peripheral blood mononuclear cells (PBMCs) in ovarian cancer via a novel "digital" cytometry technique that incorporates: (i) OC125 monoclonal antibody-conjugated gold nanoparticles as optical nanoprobes, (ii) a high contrast dark-field microscopy system to detect PBMC-bound gold nanoparticles, and (iii) a computational algorithm for automatic counting of these nanoparticles to estimate the quantity of surface-bound MUC16. The quantitative detection of our technique was successfully demonstrated by discriminating clones of the ovarian cancer cell line, OVCAR3, based on low, intermediate, and high expression levels of MUC16. Additionally, PBMC surface-bound MUC16 was tracked in an ovarian cancer patient over a 17 month period; the results suggest that the binding of MUC16 on the surface of immune cells may play an early indicator for recurrent metastasis 6 months before computational tomography-based clinical diagnosis. We also demonstrate that the levels of surface-bound MUC16 on PBMCs from five ovarian cancer patients were greater than those from five healthy controls.

Imaging and quantifying drug delivery in skin - Part 2: Fluorescence andvibrational spectroscopic imaging methods

Understanding the delivery and diffusion of topically-applied drugs on human skin is of paramount importance in both pharmaceutical and cosmetics research. This information is critical in early stages of drug development and allows the identification of the most promising ingredients delivered at optimal concentrations to their target skin compartments. Different skin imaging methods, invasive and non-invasive, are available to characterize and quantify the spatiotemporal distribution of a drug within ex vivo and in vivo human skin. The first part of this review detailed invasive imaging methods (autoradiography, MALDI and SIMS). This second part reviews non-invasive imaging methods that can be applied in vivo: i) fluorescence (conventional, confocal, and multiphoton) and second harmonic generation microscopies and ii) vibrational spectroscopic imaging methods (infrared, confocal Raman, and coherent Raman scattering microscopies). Finally, a flow chart for the selection of imaging methods is presented to guide human skin ex vivo and in vivo drug delivery studies.