MOrgAna: accessible quantitative analysis of organoids with machine learning

ABSTRACT Recent years have seen a dramatic increase in the application of organoids to developmental biology, biomedical and translational studies. Organoids are large structures with high phenotypic complexity and are imaged on a wide range of platforms, from simple benchtop stereoscopes to high-content confocal-based imaging systems. The large volumes of images, resulting from hundreds of organoids cultured at once, are becoming increasingly difficult to inspect and interpret. Hence, there is a pressing demand for a coding-free, intuitive and scalable solution that analyses such image data in an automated yet rapid manner. Here, we present MOrgAna, a Python-based software that implements machine learning to segment images, quantify and visualize morphological and fluorescence information of organoids across hundreds of images, each with one object, within minutes. Although the MOrgAna interface is developed for users with little to no programming experience, its modular structure makes it a customizable package for advanced users. We showcase the versatility of MOrgAna on several in vitro systems, each imaged with a different microscope, thus demonstrating the wide applicability of the software to diverse organoid types and biomedical studies.

Decoding ‘Unnecessary Complexity’: A Law of Complexity and a Concept of Hidden Variation Behind “Missing Heritability” in Precision Medicine

The high hopes for the Human Genome Project and personalized medicine were not met because the relationship between genotypes and phenotypes turned out to be more complex than expected. In a previous study we laid the foundation of a theory of complexity and showed that because of the blind nature of evolution, and molecular and historical contingency, cells have accumulated unnecessary complexity, complexity beyond what is necessary and sufficient to describe an organism. Here we provide empirical evidence and show that unnecessary complexity has become integrated into the genome in the form of redundancy and is relevant to molecular evolution of phenotypic complexity. Unnecessary complexity creates uncertainty between molecular and phenotypic complexity, such that phenotypic complexity (CP) is higher than molecular complexity (CM), which is higher than DNA complexity (CD). The qualitative inequality in complexity is based on the following hierarchy: CP > CM > CD. This law-like relationship holds true for all complex traits, including complex diseases. We present a hypothesis of two types of variation, namely open and closed (hidden) systems, show that hidden variation provides a hitherto undiscovered “third source” of phenotypic variation, beside genotype and environment, and argue that “missing heritability” for some complex diseases is likely to be a case of “diluted heritability”. There is a need for radically new ways of thinking about the principles of genotype–phenotype relationship. Understanding how cells use hidden, pathway variation to respond to stress can shed light on why two individuals who share the same risk factors may not develop the same disease, or how cancer cells escape death.

Data integration uncovers the metabolic bases of phenotypic variation in yeast

The relationship between different levels of integration is a key feature for understanding the genotype-phenotype map. Here, we describe a novel method of integrated data analysis that incorporates protein abundance data into constraint-based modeling to elucidate the biological mechanisms underlying phenotypic variation. Specifically, we studied yeast genetic diversity at three levels of phenotypic complexity in a population of yeast obtained by pairwise crosses of eleven strains belonging to two species, Saccharomyces cerevisiae and S. uvarum. The data included protein abundances, integrated traits (life-history/fermentation) and computational estimates of metabolic fluxes. Results highlighted that the negative correlation between production traits such as population carrying capacity (K) and traits associated with growth and fermentation rates (Jmax) is explained by a differential usage of energy production pathways: a high K was associated with high TCA fluxes, while a high Jmax was associated with high glycolytic fluxes. Enrichment analysis of protein sets confirmed our results. This powerful approach allowed us to identify the molecular and metabolic bases of integrated trait variation, and therefore has a broad applicability domain.

Genetic heterogeneity and subtypes of major depression

ABSTRACTBackgroundMajor depression (MD) is a heterogeneous disorder; however, the extent to which genetic factors distinguish MD patient subgroups (genetic heterogeneity) remains uncertain. This study sought evidence for genetic heterogeneity in MD.MethodsUsing UK Biobank cohort, the authors defined 16 MD subtypes within eight comparison groups (vegetative symptoms, symptom severity, comorbid anxiety disorder, age at onset, recurrence, suicidality, impairment and postpartum depression; N∼3,000-47,000). To compare genetic architecture of these subtypes, subtype-specific genome-wide association studies were performed to estimate SNP-heritability, and genetic correlations within subtype comparison and with other related disorders or traits.ResultsMD subtypes were divergent in their SNP-heritability, and genetic correlations both within subtype comparisons and with other related disorders/traits. Three subtype comparisons (age at onset, suicidality, and impairment) showed significant differences in SNP-heritability; while genetic correlations within subtypes comparisons ranged from 0.55 to 0.86, suggesting genetic profiles are only partially shared among MD subtypes. Furthermore, subtypes that are more clinically challenging, e.g., early-onset, recurrent, suicidal, more severely impaired, had stronger genetic correlations with other psychiatric disorders. MD with atypical features showed a positive genetic correlation (+0.40) with BMI while a negative correlation (−0.09) was found in those with non-atypical symptoms. Novel genomic loci with subtype-specific effects were identified.ConclusionsThese results provide the most comprehensive evidence to date for genetic heterogeneity within MD, and suggest that the phenotypic complexity of MD can be effectively reduced by studying the subtypes which share partially distinct etiologies.

On the evolution and development of morphological complexity: A view from gene regulatory networks

How does morphological complexity evolve? This study suggests that the likelihood of mutations increasing phenotypic complexity becomes smaller when the phenotype itself is complex. In addition, the complexity of the genotype-phenotype map (GPM) also increases with the phenotypic complexity. We show that complex GPMs and the above mutational asymmetry are inevitable consequences of how genes need to be wired in order to build complex and robust phenotypes during development.We randomly wired genes and cell behaviors into networks in EmbryoMaker. EmbryoMaker is a mathematical model of development that can simulate any gene network, all animal cell behaviors (division, adhesion, apoptosis, etc.), cell signaling, cell and tissues biophysics, and the regulation of those behaviors by gene products. Through EmbryoMaker we simulated how each random network regulates development and the resulting morphology (i.e. a specific distribution of cells and gene expression in 3D). This way we obtained a zoo of possible 3D morphologies. Real gene networks are not random, but a random search allows a relatively unbiased exploration of what is needed to develop complex robust morphologies. Compared to the networks leading to simple morphologies, the networks leading to complex morphologies have the following in common: 1) They are rarer; 2) They need to be finely tuned; 3) Mutations in them tend to decrease morphological complexity; 4) They are less robust to noise; and 5) They have more complex GPMs. These results imply that, when complexity evolves, it does so at a progressively decreasing rate over generations. This is because as morphological complexity increases, the likelihood of mutations increasing complexity decreases, morphologies become less robust to noise, and the GPM becomes more complex. We find some properties in common, but also some important differences, with non-developmental GPM models (e.g. RNA, protein and gene networks in single cells).

The Plasticity and Developmental Potential of Termites

Phenotypic plasticity provides organisms with the potential to adapt to their environment and can drive evolutionary innovations. Developmental plasticity is environmentally induced variation in phenotypes during development that arise from a shared genomic background. Social insects are useful models for studying the mechanisms of developmental plasticity, due to the phenotypic diversity they display in the form of castes. However, the literature has been biased toward the study of developmental plasticity in the holometabolous social insects (i.e., bees, wasps, and ants); the hemimetabolous social insects (i.e., the termites) have received less attention. Here, we review the phenotypic complexity and diversity of termites as models for studying developmental plasticity. We argue that the current terminology used to define plastic phenotypes in social insects does not capture the diversity and complexity of these hemimetabolous social insects. We suggest that terminology used to describe levels of cellular potency could be helpful in describing the many levels of phenotypic plasticity in termites. Accordingly, we propose a conceptual framework for categorizing the changes in potential of individuals to express alternative phenotypes through the developmental life stages of termites. We compile from the literature an exemplar dataset on the phenotypic potencies expressed within and between species across the phylogeny of the termites and use this to illustrate how the potencies of different life stages of different species can be described using this framework. We highlight how this conceptual framework can help exploit the rich phenotypic diversity of termites to address fundamental questions about the evolution and mechanisms of developmental plasticity. This conceptual contribution is likely to have wider relevance to the study of other hemimetabolous insects, such as aphids and gall-forming thrips, and may even prove useful for some holometabolous social insects which have high caste polyphenism.

On the Ecology and Distribution of Steelhead (Oncorhynchus mykiss) in California’s Eel River

The preservation of life history and other phenotypic complexity is central to the resilience of Pacific salmon stocks. Steelhead (Oncorhynchus mykiss) express a diversity of life-history strategies such as the propensity to migrate (anadromy/residency) and the timing and state of maturation upon return to freshwater (run-timing), providing an opportunity to study adaptive phenotypic complexity. Historically, the Eel River supported upwards of 1 million salmon and steelhead, but the past century has seen dramatic declines of all salmonids in the watershed. Here we investigate life-history variation in Eel River steelhead by using Rapture sequencing, on thousands of individuals, to genotype the region diagnostic for run-timing (GREB1L) and the region strongly associated with residency/anadromy (OMY5) in the Eel River and other locations, as well as determine patterns of overall genetic differentiation. Our results provide insight into many conservation-related issues. For example, we found that distinct segregation between winter and summer-run steelhead correlated with flow-dependent barriers in major forks of the Eel, that summer-run steelhead inhabited the upper Eel prior to construction of an impassable dam, and that both life history and overall genetic diversity have been maintained in the resident trout population above; and we found no evidence of the summer-run allele in the South Fork Eel, indicating that summer run-timing cannot be expected to arise from standing genetic variation in this and other populations that lack the summer-run phenotype. The results presented in this study provide valuable information for designing future restoration and management strategies for O. mykiss in Northern California and beyond.

Data integration uncovers the metabolic bases of phenotypic variation in yeast

The relationship between different levels of integration is a key feature for understanding the genotype-phenotype map.Here, we describe a novel method of integrated data analysis that incorporates protein abundance data into constraint-based modeling to elucidate the biological mechanisms underlying phenotypic variation. Specifically, we studied yeast genetic diversity at three levels of phenotypic complexity in a population of yeast obtained by pairwise crosses of eleven strains belonging to two species, Saccha-romyces cerevisiae and S. uvarum. The data included protein abundances, integrated traits (life-history/fermentation) and computational estimates of metabolic fluxes.Results highlighted that the negative correlation between production traits such as population carrying capacity (K) and traits associated with growth and fermentation rates (Jmax) is explained by a differential usage of energy production pathways: a high K was associated with high TCA fluxes, while a high Jmax was associated with high glycolytic fluxes. Enrichment analysis of protein sets confirmed our results.This powerful approach allowed us to identify the molecular and metabolic bases of integrated trait variation, and therefore has a broad applicability domain.

The Gene scb-1 Underlies Variation in Caenorhabditis elegans Chemotherapeutic Responses

Pleiotropy, the concept that a single gene controls multiple distinct traits, is prevalent in most organisms and has broad implications for medicine and agriculture. The identification of the molecular mechanisms underlying pleiotropy has the power to reveal previously unknown biological connections between seemingly unrelated traits. Additionally, the discovery of pleiotropic genes increases our understanding of both genetic and phenotypic complexity by characterizing novel gene functions. Quantitative trait locus (QTL) mapping has been used to identify several pleiotropic regions in many organisms. However, gene knockout studies are needed to eliminate the possibility of tightly linked, non-pleiotropic loci. Here, we use a panel of 296 recombinant inbred advanced intercross lines of Caenorhabditis elegans and a high-throughput fitness assay to identify a single large-effect QTL on the center of chromosome V associated with variation in responses to eight chemotherapeutics. We validate this QTL with near-isogenic lines and pair genome-wide gene expression data with drug response traits to perform mediation analysis, leading to the identification of a pleiotropic candidate gene, scb-1, for some of the eight chemotherapeutics. Using deletion strains created by genome editing, we show that scb-1, which was previously implicated in response to bleomycin, also underlies responses to other double-strand DNA break-inducing chemotherapeutics. This finding provides new evidence for the role of scb-1 in the nematode drug response and highlights the power of mediation analysis to identify causal genes.