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Abstract Super-resolution microscopy has revolutionized our ability to visualize structures below the diffraction limit of conventional optical microscopy and is particularly useful for investigating complex biological targets like chromatin. Chromatin exhibits a hierarchical organization with structural compartments and domains at different length scales, from nanometers to micrometers. Single molecule localization microscopy (SMLM) methods, such as STORM, are essential for studying chromatin at the supra-nucleosome level due to their ability to target epigenetic marks that determine chromatin organization. Multi-label imaging of chromatin is necessary to unpack its structural complexity. However, these efforts are challenged by the high-density nuclear environment, which can affect antibody binding affinities, diffusivity and non-specific interactions. Optimizing buffer conditions, fluorophore stability, and antibody specificity is crucial for achieving effective antibody conjugates. Here, we demonstrate a sequential immunolabeling protocol that reliably enables three-color studies within the dense nuclear environment. This protocol couples multiplexed localization datasets with a robust analysis algorithm, which utilizes localizations from one target as seed points for distance, density and multi-label joint affinity measurements to explore complex organization of all three targets. Applying this multiplexed algorithm to analyze distance and joint density reveals that heterochromatin and euchromatin are not-distinct territories, but that localization of transcription and euchromatin couple with the periphery of heterochromatic clusters. This work is a crucial step in molecular imaging of the dense nuclear environment as multi-label capacity enables for investigation of complex multi-component systems like chromatin with enhanced accuracy. 

Leaves are the primary harvest portion in forage crops such as alfalfa (Medicago sativa). Delaying leaf senescence is an effective strategy to improve forage biomass production and quality. In this study, we employed transcriptome sequencing to analyze the transcriptional changes and identify key senescence-associated genes under age-dependent leaf senescence in Medicago truncatula, a legume forage model plant. Through comparing the obtained expression data at different time points, we obtained 1057 differentially expressed genes, with 108 consistently up-regulated genes across leaf growth and senescence. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses showed that the 108 SAGs mainly related to protein processing, nitrogen metabolism, amino acid metabolism, RNA degradation and plant hormone signal transduction. Among the 108 SAGs, seven transcription factors were identified in which a novel bZIP transcription factor MtbZIP60 was proved to inhibit leaf senescence. MtbZIP60 encodes a nuclear-localized protein and possesses transactivation activity. Further study demonstrated MtbZIP60 could associate with MtWRKY40, both of which exhibited an up-regulated expression pattern during leaf senescence, indicating their crucial roles in the regulation of leaf senescence. Our findings help elucidate the molecular mechanisms of leaf senescence in M. truncatula and provide candidates for the genetic improvement of forage crops, with a focus on regulating leaf senescence. 

Biomanufacturing with broad applications in various industries is projected to reach a market value of ~30 trillion USD by 2030, accounting for more than one third of the global manufacturing output. Future biomanufacturing of industrial products will use novel synthetic biology tools and advanced bioprocesses to convert abundant biomass and waste resources into value-added products with comparable or superior properties to replace current petroleum-based products, thus enabling circular bioeconomy with affordable energy, economic growth, and innovation in renewable energy and chemicals production. However, biomanufacturing faces many challenges in its development that requires fundamental research in synthetic biology and novel bioprocesses involving multidisciplinary teams and academic-industry partnerships. In particular, aging and lifespan of microbial cells have been largely overlooked in industrial fermentation. Only recently have microbiologists realized that many microorganisms including yeasts (e.g., Saccharomyces cerevisiae) and bacteria (e.g., Escherichia coli) have chronological and replicative life spans which dramatically impact cell viability and longevity. In this article, we will give our perspective on how synthetic biology may contribute to overcoming some challenges facing industrial biotechnology for fuels and chemicals production from renewable sources, highlighting the importance of understanding and regulating microorganism’s lifespan and aging. 

Abstract Motivation Identification and interpretation of non-coding variations that affect disease risk remain a paramount challenge in genome-wide association studies (GWAS) of complex diseases. Experimental efforts have provided comprehensive annotations of functional elements in the human genome. On the other hand, advances in computational biology, especially machine learning approaches, have facilitated accurate predictions of cell-type-specific functional annotations. Integrating functional annotations with GWAS signals has advanced the understanding of disease mechanisms. In previous studies, functional annotations were treated as static of a genomic region, ignoring potential functional differences imposed by different genotypes across individuals. Results We develop a computational approach, Openness Weighted Association Studies (OWAS), to leverage and aggregate predictions of chromosome accessibility in personal genomes for prioritizing GWAS signals. The approach relies on an analytical expression we derived for identifying disease associated genomic segments whose effects in the etiology of complex diseases are evaluated. In extensive simulations and real data analysis, OWAS identifies genes/segments that explain more heritability than existing methods, and has a better replication rate in independent cohorts than GWAS. Moreover, the identified genes/segments show tissue-specific patterns and are enriched in disease relevant pathways. We use rheumatic arthritis and asthma as examples to demonstrate how OWAS can be exploited to provide novel insights on complex diseases. Availability and implementation The R package OWAS that implements our method is available at https://github.com/shuangsong0110/OWAS. Supplementary information Supplementary data are available at Bioinformatics online.