Quantifiable diagnosis of muscular dystrophies and neurogenic atrophies through network analysis

Entropy as a Geometrical Source of Information in Biological Organizations

Considering both biological and non-biological polygonal shape organizations, in this paper we introduce a quantitative method which is able to determine informational entropy as spatial differences between heterogeneity of internal areas from simulation and experimental samples. According to these data (i.e., heterogeneity), we are able to establish levels of informational entropy using statistical insights of spatial orders using discrete and continuous values. Given a particular state of entropy, we establish levels of information as a novel approach which can unveil general principles of biological organization. Thirty-five geometric aggregates are tested (biological, non-biological, and polygonal simulations) in order to obtain the theoretical and experimental results of their spatial heterogeneity. Geometrical aggregates (meshes) include a spectrum of organizations ranging from cell meshes to ecological patterns. Experimental results for discrete entropy using a bin width of 0.5 show that a particular range of informational entropy (0.08 to 0.27 bits) is intrinsically associated with low rates of heterogeneity, which indicates a high degree of uncertainty in finding non-homogeneous configurations. In contrast, differential entropy (continuous) results reflect negative entropy within a particular range (−0.4 to −0.9) for all bin widths. We conclude that the differential entropy of geometrical organizations is an important source of neglected information in biological systems.

Individual-specific networks for prediction modelling – A scoping review of methods

Background

Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space.

Methods

We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000–2020 in the electronic databases PubMed, Scopus and Embase.

Results

Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual’s contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction.

Conclusion

The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01544-6.

Mechanics and self-organization in tissue development

Self-organization is an all-important feature of living systems that provides the means to achieve specialization and functionality at distinct spatio-temporal scales. Herein, we review this concept by addressing the packing organization of cells, the sorting/compartmentalization phenomenon of cell populations, and the propagation of organizing cues at the tissue level through traveling waves. We elaborate on how different theoretical models and tools from Topology, Physics, and Dynamical Systems have improved the understanding of self-organization by shedding light on the role played by mechanics as a driver of morphogenesis. Altogether, by providing a historical perspective, we show how ideas and hypotheses in the field have been revisited, developed, and/or rejected and what are the open questions that need to be tackled by future research.

Approach for semi-automated measurement of fiber diameter in murine and canine skeletal muscle

Currently available software tools for automated segmentation and analysis of muscle cross-section images often perform poorly in cases of weak or non-uniform staining conditions. To address these issues, our group has developed the MyoSAT (Myofiber Segmentation and Analysis Tool) image-processing pipeline. MyoSAT combines several unconventional approaches including advanced background leveling, Perona-Malik anisotropic diffusion filtering, and Steger’s line detection algorithm to aid in pre-processing and enhancement of the muscle image. Final segmentation is based upon marker-based watershed segmentation. Validation tests using collagen V labeled murine and canine muscle tissue demonstrate that MyoSAT can determine mean muscle fiber diameter with an average accuracy of ~92.4%. The software has been tested to work on full muscle cross-sections and works well even under non-optimal staining conditions. The MyoSAT software tool has been implemented as a macro for the freely available ImageJ software platform. This new segmentation tool allows scientists to efficiently analyze large muscle cross-sections for use in research studies and diagnostics.

The topology of vitronectin: A complementary feature for neuroblastoma risk classification based on computer-aided detection

Tumors are complex networks of constantly interacting elements: tumor cells, stromal cells, immune and stem cells, blood/lympathic vessels, nerve fibers and extracellular matrix components. These elements can influence their microenvironment through mechanical and physical signals to promote tumor cell growth. To get a better understanding of tumor biology, cooperation between multidisciplinary fields is needed. Diverse mathematic computations and algorithms have been designed to find prognostic targets and enhance diagnostic assessment. In this work, we use computational digital tools to study the topology of vitronectin, a glycoprotein of the extracellular matrix. Vitronectin is linked to angiogenesis and migration, two processes closely related to tumor cell spread. Here, we investigate whether the distribution of this molecule in the tumor stroma may confer mechanical properties affecting neuroblastoma aggressiveness. Combining image analysis and graph theory, we analyze different topological features that capture the organizational cues of vitronectin in histopathological images taken from human samples. We find that the Euler number and the branching of territorial vitronectin, two topological features, could allow for a more precise pretreatment risk stratification to guide treatment strategies in neuroblastoma patients. A large amount of recently synthesized VN would create migration tracks, pinpointed by both topological features, for malignant neuroblasts, so that dramatic change in the extracellular matrix would increase tumor aggressiveness and worsen patient outcomes.

Smoke-induced neuromuscular junction degeneration precedes the fibre type shift and atrophy in chronic obstructive pulmonary disease

KEY POINTS

Chronic obstructive pulmonary disease (COPD) is largely caused by smoking, and patient limb muscle exhibits a fast fibre shift and atrophy. We show that this fast fibre shift is associated with type grouping, suggesting recurring cycles of denervation-reinnervation underlie the type shift. Compared to patients with normal fat-free mass index (FFMI), patients with low FFMI exhibited an exacerbated fibre type shift, marked accumulation of very small persistently denervated muscle fibres, and a blunted denervation-responsive transcript profile, suggesting failed denervation precipitates muscle atrophy in patients with low FFMI. Sixteen weeks of passive tobacco smoke exposure in mice caused neuromuscular junction degeneration, consistent with a key role for smoke exposure in initiating denervation in COPD.

ABSTRACT

A neurological basis for the fast fibre shift and atrophy seen in limb muscle of patients with chronic obstructive pulmonary disease (COPD) has not been considered previously. The objective of our study was: (1) to determine if denervation contributes to fast fibre shift and muscle atrophy in COPD; and (2) to assess using a preclinical smoking mouse model whether chronic tobacco smoke (TS) exposure could initiate denervation by causing neuromuscular junction (NMJ) degeneration. Vastus lateralis muscle biopsies were obtained from severe COPD patients [n = 10 with low fat-free mass index (FFMI), 65 years; n = 15 normal FFMI, 65 years) and healthy age- and activity-matched non-smoker control subjects (CON; n = 11, 67 years), to evaluate morphological and transcriptional markers of denervation. To evaluate the potential for chronic TS exposure to initiate these changes, we examined NMJ morphology in male adult mice following 16 weeks of passive TS exposure. We observed a high proportion of grouped fast fibres and a denervation transcript profile in COPD patients, suggesting that motor unit remodelling drives the fast fibre type shift in COPD patient limb muscle. A further exacerbation of fast fibre grouping in patients with low FFMI, coupled with blunted reinnervation signals, accumulation of very small non-specific esterase hyperactive fibres and neural cell adhesion molecule-positive type I and type II fibres, suggests denervation-induced exhaustion of reinnervation contributes to muscle atrophy in COPD. Evidence from a smoking mouse model showed significant NMJ degeneration, suggesting that recurring denervation in COPD is probably caused by decades of chronic TS exposure.

MyoVision: software for automated high-content analysis of skeletal muscle immunohistochemistry

Analysis of skeletal muscle cross sections is an important experimental technique in muscle biology. Many aspects of immunohistochemistry and fluorescence microscopy can now be automated, but most image quantification techniques still require extensive human input, slowing progress and introducing the possibility of user bias. MyoVision is a new software package that was developed to overcome these limitations. The software improves upon previously reported automatic techniques and analyzes images without requiring significant human input and correction. When compared with data derived by manual quantification, MyoVision achieves an accuracy of ≥94% for basic measurements such as fiber number, fiber type distribution, fiber cross-sectional area, and myonuclear number. Scientists can download the software free from www.MyoVision.org and use it to automate the analysis of their own experimental data. This will improve the efficiency and consistency of the analysis of muscle cross sections and help to reduce the burden of routine image quantification in muscle biology. NEW & NOTEWORTHY Scientists currently analyze images of immunofluorescently labeled skeletal muscle using time-consuming techniques that require sustained human supervision. As well as being inefficient, these techniques can increase variability in studies that quantify morphological adaptations of skeletal muscle at the cellular level. MyoVision is new software that overcomes these limitations by performing high-content analysis of muscle cross sections with minimal manual input. It is open source and freely available.

Rules of tissue packing involving different cell types: human muscle organization

Natural packed tissues are assembled as tessellations of polygonal cells. These include skeletal muscles and epithelial sheets. Skeletal muscles appear as a mosaic composed of two different types of cells: the "slow" and "fast" fibres. Their relative distribution is important for the muscle function but little is known about how the fibre arrangement is established and maintained. In this work we capture the organizational pattern in two different healthy muscles: biceps brachii and quadriceps. Here we show that the biceps brachii muscle presents a particular arrangement, based on the different sizes of slow and fast fibres. By contrast, in the quadriceps muscle an unbiased distribution exists. Our results indicate that the relative size of each cellular type imposes an intrinsic organization into natural tessellations. These findings establish a new framework for the analysis of any packed tissue where two or more cell types exist.

Fundamental physical cellular constraints drive self-organization of tissues

Morphogenesis is driven by small cell shape changes that modulate tissue organization. Apical surfaces of proliferating epithelial sheets have been particularly well studied. Currently, it is accepted that a stereotyped distribution of cellular polygons is conserved in proliferating tissues among metazoans. In this work, we challenge these previous findings showing that diverse natural packed tissues have very different polygon distributions. We use Voronoi tessellations as a mathematical framework that predicts this diversity. We demonstrate that Voronoi tessellations and the very different tissues analysed share an overriding restriction: the frequency of polygon types correlates with the distribution of cell areas. By altering the balance of tensions and pressures within the packed tissues using disease, genetic or computer model perturbations, we show that as long as packed cells present a balance of forces within tissue, they will be under a physical constraint that limits its organization. Our discoveries establish a new framework to understand tissue architecture in development and disease.

Fhl1 W122S causes loss of protein function and late-onset mild myopathy

A member of the four-and-a-half-LIM (FHL) domain protein family, FHL1, is highly expressed in human adult skeletal and cardiac muscle. Mutations in FHL1 have been associated with diverse X-linked muscle diseases: scapuloperoneal (SP) myopathy, reducing body myopathy, X-linked myopathy with postural muscle atrophy, rigid spine syndrome (RSS) and Emery-Dreifuss muscular dystrophy. In 2008, we identified a missense mutation in the second LIM domain of FHL1 (c.365 G>C, p.W122S) in a family with SP myopathy. We generated a knock-in mouse model harboring the c.365 G>C Fhl1 mutation and investigated the effects of this mutation at three time points (3-5 months, 7-10 months and 18-20 months) in hemizygous male and heterozygous female mice. Survival was comparable in mutant and wild-type animals. We observed decreased forelimb strength and exercise capacity in adult hemizygous male mice starting from 7 to 10 months of age. Western blot analysis showed absence of Fhl1 in muscle at later stages. Thus, adult hemizygous male, but not heterozygous female, mice showed a slowly progressive phenotype similar to human patients with late-onset muscle weakness. In contrast to SP myopathy patients with the FHL1 W122S mutation, mutant mice did not manifest cytoplasmic inclusions (reducing bodies) in muscle. Because muscle weakness was evident prior to loss of Fhl1 protein and without reducing bodies, our findings indicate that loss of function is responsible for the myopathy in the Fhl1 W122S knock-in mice.

Biotensegrity of the extracellular matrix: physiology, dynamic mechanical balance, and implications in oncology and mechanotherapy

Cells have the capacity to convert mechanical stimuli into chemical changes. This process is based on the tensegrity principle, a mechanism of tensional integrity. To date, this principle has been demonstrated to act in physiological processes such as mechanotransduction and mechanosensing at different scales (from cell sensing through integrins to molecular mechanical interventions or even localized massage). The process involves intra- and extracellular components, including the participation of extracellular matrix (ECM) and microtubules that act as compression structures, and actin filaments which act as tension structures. The nucleus itself has its own tensegrity system which is implicated in cell proliferation, differentiation, and apoptosis. Despite present advances, only the tip of the iceberg has so far been uncovered regarding the role of ECM compounds in influencing biotensegrity in pathological processes. Groups of cells, together with the surrounding ground substance, are subject to different and specific forces that certainly influence biological processes. In this paper, we review the current knowledge on the role of ECM elements in determining biotensegrity in malignant processes and describe their implication in therapeutic response, resistance to chemo- and radiotherapy, and subsequent tumor progression. Original data based on the study of neuroblastic tumors will be provided.

Cooperation and competition in the dynamics of tissue architecture during homeostasis and tumorigenesis

The construction of a network of cell-to-cell contacts makes it possible to characterize the patterns and spatial organization of tissues. Such networks are highly dynamic, depending on the changes of the tissue architecture caused by cell division, death and migration. Local competitive and cooperative cell-to-cell interactions influence the choices cells make. We review the literature on quantitative data of epithelial tissue topology and present a dynamical network model that can be used to explore the evolutionary dynamics of a two dimensional tissue architecture with arbitrary cell-to-cell interactions. In particular, we show that various forms of experimentally observed types of interactions can be modelled using game theory. We discuss a model of cooperative and non-cooperative cell-to-cell communication that can capture the interplay between cellular competition and tissue dynamics. We conclude with an outlook on the possible uses of this approach in modelling tumorigenesis and tissue homeostasis.