Understanding Health and Disease with Multidimensional Single-Cell Methods

Current efforts in the biomedical sciences and related interdisciplinary fields are focused on gaining a molecular understanding of health and disease, which is a problem of daunting complexity that spans many orders of magnitude in characteristic length scales, from small molecules that regulate cell function to cell ensembles that form tissues and organs working together as an organism. In order to uncover the molecular nature of the emergent properties of a cell, it is essential to measure multiple cell components simultaneously in the same cell. In turn, cell heterogeneity requires multiple cells to be measured in order to understand health and disease in the organism. This review summarizes current efforts towards a data-driven framework that leverages single-cell technologies to build robust signatures of healthy and diseased phenotypes. While some approaches focus on multicolor flow cytometry data and other methods are designed to analyze high-content image-based screens, we emphasize the so-called Supercell/SVM paradigm (recently developed by the authors of this review and collaborators) as a unified framework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond their specific contributions to basic and translational biomedical research, these efforts illustrate, from a larger perspective, the powerful synergy that might be achieved from bringing together methods and ideas from statistical physics, data mining, and mathematics to solve the most pressing problems currently facing the life sciences.Comment: 25 pages, 7 figures; revised version with minor changes. To appear in J. Phys.: Cond. Mat

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

The characteristics of potatoes differing in glycemic index

This thesis describes studies on seven potato cultivars with the objective of identifying a potato cultivar with a low glycemic index (GI), and to describe its tuber and starch properties. The potato cultivars were selected in consultation with potato breeders from Agrico Holland and sourced from growers in South Australia (The Mitolo Group) and Tasmania (Agronico) Australia and consisted of well established (Bintje, Desiree, Nicola, Russet Burbank) and newly introduced commercial cultivars (Carisma, Maiflower, Virginia Rose). The potato cultivars were tested for their GI according to International Standard Organisation (ISO) guidelines. In vitro enzymatic starch hydrolysis and chemical analyses were performed for each potato cultivar and correlations sought with the respective GI values. Different imaging techniques were used to study and compare cell structure and native starch granule morphology, and the effect of cooking on cell wall structure and starch gelatinization. Physicochemical and functional properties of starch from the seven potato cultivars were analyzed for amylose content, amylopectin chain length distribution, relative crystallinity, phosphorus content, granule size distribution, thermal properties and starch pasting profiles. Physicochemical, thermal and pasting properties of starch from the same cultivars of potatoes grown in the Netherlands under very different conditions were also examined

Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era

Nowadays, morphology and molecular analyses at the single-cell level have a fundamental role in understanding biology better. These methods are utilized for cell phenotyping and in-depth studies of cellular processes, such as mitosis. Fluorescence microscopy and optical spectroscopy techniques, including Raman micro-spectroscopy, allow researchers to examine biological samples at the single-cell level in a non-destructive manner. Fluorescence microscopy can give detailed morphological information about the localization of stained molecules, while Raman microscopy can produce label-free images at the subcellular level; thus, it can reveal the spatial distribution of molecular fingerprints, even in live samples. Accordingly, the combination of correlative fluorescence and Raman microscopy (CFRM) offers a unique approach for studying cellular stages at the singlecell level. However, subcellular spectral maps are complex and challenging to interpret. Artificial intelligence (AI) may serve as a valuable solution to characterize the molecular backgrounds of phenotypes and biological processes by finding the characteristic patterns in spectral maps. The major contributions of the manuscript are: (I) it gives a comprehensive review of the literature focusing on AI techniques in Raman-based cellular phenotyping; (II) via the presentation of a case study, a new neural network-based approach is described, and the opportunities and limitations of AI, specifically deep learning, are discussed regarding the analysis of Raman spectroscopy data to classify mitotic cellular stages based on their spectral maps

Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy.

Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins

Characterization Of T-Cell Ontogeny And Other Cell Populations In Pediatric Human Thymus

Immune related diseases, including autoimmune disorders, allergies, graft rejection, tumour growth are still one of the main concerns of current medicine. The complexity of the immune system demands a continuous review of the knowledge employing available technologies. T lymphocytes play a central role in the coordination, regulation and execution of immune responses. Maturation of T lymphocytes from the thymus towards the periphery elicit several questions regarding the relations between different immune cell populations, which are highly influenced by the processes taking place at the thymus. Such processes, related to differences into immature and mature cell populations of thymocytes, showing different patterns of expression of CD4 and CD8, along with other factors, could reveal useful information still unknown with respect to the spatial distribution and interactions of such cells. In addition, regulatory T cells (Treg) is a subtype of T cell specialised in the regulation of immune responses, and its ontogeny in the thymus also represent an interesting unexplored concept. Here we show extensive and detailed analysis based on characterization and identification of the distribution of different cell populations of thymocytes at the thymus, derived from flow cytometry and 2D images acquired by confocal microscopy. The distribution of the four major thymic cell populations found is identified, ensuring results reported by previous publications, along with spatial identification of thymocytes, especially focusing on the interactions related to regulatory T cells. Our results demonstrate further reassurance of the presence of different patterns of FOXP3 expression, the marker that characterises Tregs, as well as the inability of CD45RA and CD45RO markers for providing reliable information at the thymus. Therefore, analysis on the results obtained from a comparison between flow cytometry and confocal microscopy have high reliability and consistency with previous results reported, reinforcing the importance of identification, selection and maturation processes related to Treg cell population for further research focused on autoimmunity and maintenance of self-toleranceIngeniería Biomédic

Automated Identification and Measurement of Haematopoietic Stem Cells in 3D Intravital Microscopy Data

Image analysis and quantification of Haematopoietic stem cells (HSCs) position within their surrounding microenvironment in the bone marrow is a fast growing area of research, as it holds the key to understanding the dynamics of HSC-niche interactions and their multiple implications in normal tissue development and in response to various stress events. However, this area of research is very challenging due to the complex cellular structure of such images. Therefore, automated image analysis tools are required to simplify the biological interpretation of 3D HSC microenvironment images. In this chapter, we describe how 3D intravital microscopy data can be visualised and analysed using a computational method that allows the automated quantification of HSC position relative to surrounding niche components

ELECTRICAL IDENTIFICATION OF INNATE IMMUNE CELLS

This thesis is concerned with the electrical characterization of the key players of the innate immune cells. Innate immunity is basically the nonspecific immune response that is triggered by any foreign body that attacks the human system. The key players of the innate immune system are mainly dendritic cells and macrophages. Accurately classifying these cell types helps us understand the mechanism of the immune system thereby enabling the development of models to improve new prospects for therapeutics and diagnostics. The characterization described in this thesis is based on extracting the capacitance for each biological cell using I-V curves. The main aim of this thesis is to overcome the drawbacks of the conventional techniques used to characterize and differentiate between these immune cells. The main challenges with the conventional techniques are the problem of cross-referencing and the lack of biological identity for each cell. A MatLab mathematical model was developed to extract the capacitance from the I-V curves obtained from the DropSend machine. The results obtained to display the concept of immunophenotyping, meaning the ability to accurately define and characterize the different types of cells. Our results for the capacitance are in concordance with the area for each cell type and the published literature