The islets of Langerhans are critical endocrine micro-organs that secrete hormones regulating energy metabolism in animals. Insulin and glucagon, secreted by beta and alpha cells, respectively, are ...responsible for metabolic switching between fat and glucose utilization. Dysfunction in their secretion and/or counter-regulatory influence leads to diabetes. Debate in the field centers on the cytoarchitecture of islets, as the signaling that governs hormonal secretion depends on structural and functional factors, including electrical connectivity, innervation, vascularization, and physical proximity. Much effort has therefore been devoted to elucidating which architectural features are significant for function and how derangements in these features are correlated or causative for dysfunction, especially using quantitative network science or graph theory characterizations. Here, we ask if there are non-local features in islet cytoarchitecture, going beyond standard network statistics, that are relevant to islet function. An example is ring structures, or cycles, of α and δ cells surrounding β cell clusters or the opposite, β cells surrounding α and δ cells. These could appear in two-dimensional islet section images if a sphere consisting of one cell type surrounds a cluster of another cell type. To address these issues, we developed two independent computational approaches, geometric and topological, for such characterizations. For the latter, we introduce an application of topological data analysis to determine locations of topological features that are biologically significant. We show that both approaches, applied to a large collection of islet sections, are in complete agreement in the context both of developmental and diabetes-related changes in islet characteristics. The topological approach can be applied to three-dimensional imaging data for islets as well.
Pancreatic islets of Langerhans consist of endocrine cells, primarily α, β and δ cells, which secrete glucagon, insulin, and somatostatin, respectively, to regulate plasma glucose. β cells form ...irregular locally connected clusters within islets that act in concert to secrete insulin upon glucose stimulation. Due to the central functional significance of this local connectivity in the placement of β cells in an islet, it is important to characterize it quantitatively. However, quantification of the seemingly stochastic cytoarchitecture of β cells in an islet requires mathematical methods that can capture topological connectivity in the entire β-cell population in an islet. Graph theory provides such a framework. Using large-scale imaging data for thousands of islets containing hundreds of thousands of cells in human organ donor pancreata, we show that quantitative graph characteristics differ between control and type 2 diabetic islets. Further insight into the processes that shape and maintain this architecture is obtained by formulating a stochastic theory of β-cell rearrangement in whole islets, just as the normal equilibrium distribution of the Ornstein-Uhlenbeck process can be viewed as the result of the interplay between a random walk and a linear restoring force. Requiring that rearrangements maintain the observed quantitative topological graph characteristics strongly constrained possible processes. Our results suggest that β-cell rearrangement is dependent on its connectivity in order to maintain an optimal cluster size in both normal and T2D islets.
The growing complexity of biological data has spurred the development of innovative computational techniques to extract meaningful information and uncover hidden patterns within vast datasets. ...Biological networks, such as gene regulatory networks and protein-protein interaction networks, hold critical insights into biological features’ connections and functions. Integrating and analyzing high-dimensional data, particularly in gene expression studies, stands prominent among the challenges in deciphering these networks. Clustering methods play a crucial role in addressing these challenges, with spectral clustering emerging as a potent unsupervised technique considering intrinsic geometric structures. However, spectral clustering’s user-defined cluster number can lead to inconsistent and sometimes orthogonal clustering regimes. We propose the
Multi-layer Bundling (MLB)
method to address this limitation, combining multiple prominent clustering regimes to offer a comprehensive data view. We call the outcome clusters “bundles”. This approach refines clustering outcomes, unravels hierarchical organization, and identifies bridge elements mediating communication between network components. By layering clustering results, MLB provides a global-to-local view of biological feature clusters enabling insights into intricate biological systems. Furthermore, the method enhances bundle network predictions by integrating the
bundle co-cluster matrix
with the affinity matrix. The versatility of MLB extends beyond biological networks, making it applicable to various domains where understanding complex relationships and patterns is needed.
Abstract
Sepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical for patient stratification. Lack of ...accurate sepsis endotyping impedes clinicians from making timely decisions alongside insufficiencies in appropriate sepsis management. This work aims to demonstrate the potential feasibility of a data-driven validation model for supporting clinical decisions to predict sepsis host-immune response. Herein, we used a machine learning approach to determine the predictive potential of identifying sepsis host immune response for patient stratification by combining multiple biomarker measurements from a single plasma sample. Results were obtained using the following cytokines and chemokines IL-6, IL-8, IL-10, IP-10 and TRAIL where the test dataset was 70%. Supervised machine learning algorithm naïve Bayes and decision tree algorithm showed good accuracy of 96.64% and 94.64%. These promising findings indicate the proposed AI approach could be a valuable testing resource for promoting clinical decision making.
Our limited understanding of the pathophysiological mechanisms that operate during sepsis is an obstacle to rational treatment and clinical trial design. There is a critical lack of data from low- ...and middle-income countries where the sepsis burden is increased which inhibits generalized strategies for therapeutic intervention. Here we perform RNA sequencing of whole blood to investigate longitudinal host response to sepsis in a Ghanaian cohort. Data dimensional reduction reveals dynamic gene expression patterns that describe cell type-specific molecular phenotypes including a dysregulated myeloid compartment shared between sepsis and COVID-19. The gene expression signatures reported here define a landscape of host response to sepsis that supports interventions via targeting immunophenotypes to improve outcomes.
A nucleotide sequence 35 base pairs long can take 1,180,591,620,717,411,303,424 possible values. An example of systems biology datasets, protein binding microarrays, contain activity data from about ...40,000 such sequences. The discrepancy between the number of possible configurations and the available activities is enormous. Thus, albeit that systems biology datasets are large in absolute terms, they oftentimes require methods developed for rare events due to the combinatorial increase in the number of possible configurations of biological systems. A plethora of techniques for handling large datasets, such as Empirical Bayes, or rare events, such as importance sampling, have been developed in the literature, but these cannot always be simultaneously utilized. Here we introduce a principled approach to Empirical Bayes based on importance sampling, information theory, and theoretical physics in the general context of sequence phenotype model induction. We present the analytical calculations that underlie our approach. We demonstrate the computational efficiency of the approach on concrete examples, and demonstrate its efficacy by applying the theory to publicly available protein binding microarray transcription factor datasets and to data on synthetic cAMP-regulated enhancer sequences. As further demonstrations, we find transcription factor binding motifs, predict the activity of new sequences and extract the locations of transcription factor binding sites. In summary, we present a novel method that is efficient (requiring minimal computational time and reasonable amounts of memory), has high predictive power that is comparable with that of models with hundreds of parameters, and has a limited number of optimized parameters, proportional to the sequence length.
•We developed a novel method for quantitative phenotype inference from large sequence datasets.•Our method computes higher order interaction parameters from the data without optimization.•We demonstrated the efficacy of the method by applying it to published datasets.•We showed that it is both computationally efficient and has high predictive power.
The mechanism for cortical folding pattern formation is not fully understood. Current models represent scenarios that describe pattern formation through local interactions, and one recent model is ...the intermediate progenitor model. The intermediate progenitor (IP) model describes a local chemically driven scenario, where an increase in intermediate progenitor cells in the subventricular zone correlates to gyral formation. Here we present a mathematical model that uses features of the IP model and further captures global characteristics of cortical pattern formation. A prolate spheroidal surface is used to approximate the ventricular zone. Prolate spheroidal harmonics are applied to a Turing reaction-diffusion system, providing a chemically based framework for cortical folding. Our model reveals a direct correlation between pattern formation and the size and shape of the lateral ventricle. Additionally, placement and directionality of sulci and the relationship between domain scaling and cortical pattern elaboration are explained. The significance of this model is that it elucidates the consistency of cortical patterns among individuals within a species and addresses inter-species variability based on global characteristics and provides a critical piece to the puzzle of cortical pattern formation.
Sequence length heterogeneity (SLH) is defined as the change, as a function of copolymer molar mass (
M), in the average number of continuous monomers of a given repeat unit. SLH can influence ...polymeric properties such as thermal stability, mechanical behavior, transparency, and the ability of copolymers to reduce interfacial surface tension. Here, we demonstrate the relation between SLH and the change as a function of molar mass of a dimensionless size parameter, the ratio of the viscometric radius and the radius of gyration, irrespective of chemical heterogeneity or molar mass polydispersity. Multi-detector size-exclusion chromatography (SEC) provides for a convenient method by which to experimentally establish this relation and, consequently, a method by which to determine whether SLH is present in a copolymer, whether the degree of randomness of a copolymer changes across the molar mass distribution (MMD), or whether two copolymers differ from each other in degree of randomness at a given
M and/or across their MMDs. Results from our SEC and FT-IR measurements of block, random, alternating, and gradient copolymers of styrene (S) and methyl methacrylate (MMA) and their respective homopolymers agree with results from a probability theory based model of SLH in linear random copolymers. The multi-detector SEC method employs instrumentation available in most polymer separations laboratories and the relations developed should be applicable to copolymers other than the S-MMAs studied here.
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Plasma glucose in mammals is regulated by hormones secreted by the islets of Langerhans embedded in the exocrine pancreas. Islets consist of endocrine cells, primarily α, β, and δ cells, which ...secrete glucagon, insulin, and somatostatin, respectively. β cells form irregular locally connected clusters within islets that act in concert to secrete insulin upon glucose stimulation. Varying demands and available nutrients during development produce changes in the local connectivity of β cells in an islet. We showed in earlier work that graph theory provides a framework for the quantification of the seemingly stochastic cyto-architecture of β cells in an islet. To quantify the dynamics of endocrine connectivity during development requires a framework for characterizing changes in the probability distribution on the space of possible graphs, essentially a Fokker-Planck formalism on graphs. With large-scale imaging data for hundreds of thousands of islets containing millions of cells from human specimens, we show that this dynamics can be determined quantitatively. Requiring that rearrangement and cell addition processes match the observed dynamic developmental changes in quantitative topological graph characteristics strongly constrained possible processes. Our results suggest that there is a transient shift in preferred connectivity for β cells between 1-35 weeks and 12-24 months.
For incompletely resolved peak pairs, the purity of the chromatographic or fractographic fractions is oftentimes underestimated by the common user. This results in wasted time and effort while trying ...to achieve higher resolution than needed for the intended use. While a choice regarding acceptable fraction purity is ultimately up to the user and will be dictated by the purpose for which the separation is being conducted, knowledge of fraction purity as a function of chromatographic resolution
R
s
can help make an informed decision in this regard. To this effect, we revisit here the relationship between peak fraction purity and
R
s
for pairs of Gaussian peaks, equal pairs ranging in
R
s
from 0.42 to 1.68 and unequal pairs of various analyte ratios and
R
s
values. Employing sophisticated yet highly accessible commercial software, we calculate, to a greater precision than previously reported, the purity resultant from midpoint or valley cuts of peak pairs, and also show the improvement gained from performing these cuts at either the maxima of the cumulative peak or at the locations in this peak corresponding to the centers of gravity of the individual component peaks. The methodology employed and equations given are applicable to
R
s
values other than those investigated here and can be employed to calculate cut-point estimates for virtually any arbitrary desired purity.
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