bluuebunny/crossref_metadata_2025_split huggingface dataset:
A framework for ultra-low input spatial tissue proteomics
Authors: Makhmut Anuar , Qin Di , Fritzsche Sonja , Nimo Jose , König Janett , Coscia Fabian 
Published: May 2023

ABSTRACTSpatial tissue proteomics combining microscopy-based cell phenotyping with ultra-sensitive mass spectrometry (MS)-based proteomics is an emerging and powerful concept for the study of cell function and heterogeneity in health and disease. However, optimized workflows that preserve morphological information for image-based phenotype discovery and maximize proteome coverage of few or even single cells from laser microdissected archival tissue, are currently lacking. Here, we report a robust and scalable workflow for the proteomic analysis of ultra-low input formalin-fixed, paraffin-embedded (FFPE) material. Benchmarking in the murine liver resulted in up to 2,000 quantified proteins from single hepatocyte contours and nearly 5,000 proteins from 50-cell regions with high quantitative reproducibility. Applied to human tonsil, we profiled 146 microregions including spatially defined T and B lymphocyte niches and quantified cell type specific markers, cytokines, immune cell regulators and transcription factors. These rich data also highlighted proteome dynamics in spatially defined zones of activated germinal centers, illuminating sites undergoing active B-cell proliferation and somatic hypermutation. Our results demonstrate the power of spatially-resolved proteomics for tissue phenotyping by integrating high-content imaging, laser microdissection, and ultra-sensitive mass spectrometry. This approach has broad implications for a wide range of biomedical applications, including early disease profiling, drug target discovery and biomarker research.
Zymoseptoria triticisuppresses the host immune response and facilitates the success of avirulent strains in mixed infections
Authors: Bernasconi Alessio , Lorrain Cécile , Flury Priska , Alassimone Julien , McDonald Bruce A. , Sánchez-Vallet Andrea 
Published: May 2023

SummaryPlants interact with a plethora of pathogenic microorganisms in nature. Pathogen-plant interaction experiments focus mainly on single-strain infections, typically ignoring the complexity of multi-strain infections even though mixed infections are common and critical for the infection outcome. The wheat pathogenZymoseptoria triticiforms highly diverse fungal populations in which several pathogen strains often colonize the same leaf. Despite the importance of mixed infections, the mechanisms governing interactions between a mixture of pathogen strains within a plant host remain largely unexplored. Here we demonstrate that avirulent pathogen strains benefit from being in mixed infections with virulent strains. We show that virulent strains suppress the wheat immune response, allowing the avirulent strain to colonize the apoplast and to reproduce. Our experiments indicate that virulent strains in mixed infections can challenge the plant immune system both locally and systemically, providing a mechanistic explanation for the persistence of avirulent pathogen strains in fields planted to resistant host plants.
MSCProfiler: An image processing workflow to investigate Mesenchymal Stem Cell heterogeneity using imaging flow cytometry data
Authors: Gupta A. , Shaik S.K. , Balasubramanian L. , Chakraborty U 
Published: May 2023

ABSTRACTSingle-cell immuno-heterogeneity has always been the forerunner of any change in homeostasis of cellular functions in the body. Mesenchymal stem cells represent a viable source for the development of cell-based therapies. Multiple conditions giving rise to inter, and intra-population variations result in heterogeneity and multipotent differentiation ability of these cells of stromal origin. Cell surface markers which are important members of membrane proteins, ion channels, transporter, adhesion, and signaling molecules generally differentiate between stromal cells of different origin. However, existing analytical tools cannot always model a pattern of their surface distribution in successive generations of growth and proliferation. In this study, we have developed a post-acquisition image analysis pipeline for human mesenchymal stromal cells obtained from exfoliated deciduous teeth (hSHEDs). Using the open-source image processing software CellProfiler, a pipeline has been developed to extract cellular features from 50,000-100,000 single-cell images. We made use of the image flow cytometry technology to explore the morphometric properties of hSHEDs, along with their surface marker distribution. This unbiased pipeline can extract cellular, geometrical and texture features such as shape, size, eccentricity, entropy, intensities as a measure of cellular heterogeneity. For the first time, we have described an automated, unbiased image assessment protocol implemented in a validated open-source software, leveraging the suite of image-based measurements to develop the prototype named as MSCProfiler. The hallmark of this screening workflow has been the identification and removal of image-based aberrations to identify the single-cell bright field and fluorescent images of mesenchymal stem cells.
Interindividual Diversity of Human Gut Mucin-Degrading Microbial Consortia
Authors: Fricker Ashwana D. , Yao Tianming , Lindemann Stephen R. , Flores Gilberto E. 
Published: May 2023

AbstractMucin is a glycoprotein secreted throughout the mammalian gastrointestinal tract that can support endogenous microorganisms in the absence of complex polysaccharides. While diverse mucin degrading bacteria have been identified, the individual host microbial community differences capable of metabolizing this complex polymer are not well described. To determine whether individuals have taxonomically distinct but functionally similar mucin-degrading communities, we used a ten-dayin vitrosequential batch culture fermentation from three human donors with mucin as the sole carbon source. For each donor, 16S rRNA gene amplicon sequencing was used to characterize microbial community succession, and the short-chain fatty acid profile was determined from the final community. Although two of the final communities had genus-level taxonomic differences signified by the presence ofDesulfovibrioandAkkermansia, other members, such asBacteroides, were shared between all three final communities. Metabolic output differences were most notable for one of the donor’s communities, with significantly less production of acetate and propionate than the other two communities. These findings reinforce the concept of a taxonomically distinct and, at broad levels, a functionally redundant gut microbiome. Furthermore, the mechanisms and efficiencies of mucin degradation across individuals are important for understanding how this community-level process impacts human health.
Visualization of the Cdc48 AAA+ ATPase protein unfolding pathway
Authors: Cooney Ian , Schubert Heidi L. , Cedeno Karina , Lin Hsien-Jung L. , Price John C , Hill Christopher P , Shen Peter S 
Published: May 2023

AbstractThe Cdc48 AAA+ ATPase is an abundant and essential enzyme that unfolds substrates in multiple protein quality control pathways. The enzyme includes two conserved AAA+ ATPase cassettes, D1 and D2, that assemble as hexameric rings with D1 stacked above D2. Here, we report an ensemble of structures of Cdc48 affinity purified from lysate in complex with the adaptor Shp1 in the act of unfolding substrate. Our analysis reveals a continuum of structural snapshots that spans the entire translocation cycle. These data reveal new elements of Shp1-Cdc48 binding and support a “hand-over-hand” mechanism in which the sequential movement of individual subunits is closely coordinated. D1 hydrolyzes ATP and disengages from substrate prior to D2, while D2 rebinds ATP and re-engages with substrate prior to D1, thereby explaining the dominant role played by D2 in substrate translocation/unfolding.
Living fast, dying young: anthropogenic habitat modification influences the fitness and life history traits of a cooperative breeder
Authors: Alaman Alejandro , Casas Enrique , Arbelo Manuel , Keynan Oded , Koren Lee 
Published: May 2023

AbstractModification of natural habitats can lead to an ecological trap, as animals can maladaptively select lower-quality habitats over natural landscapes. Modified habitats reduce group size and prevalence, the success of dispersing individuals, and breeding success. However, little is known about how these effects on fitness affect the sociality of cooperative breeding species, which may be particularly susceptible to habitat modification. Here we show that the selection of anthropogenically modified habitats negatively affects the fitness, which has consequences in the life history traits of a cooperative breeder.We used data collected during six years from a monitored population of Arabian babblers (Argya squamiceps) and found that groups living in modified habitats breed more than those in natural habitats but that juvenile and adult survival in modified habitats was lower. Individuals living in modified habitats acquired dominance earlier than individuals from natural habitats. Males in modified habitats also dispersed earlier than those from natural habitats.Our results suggest that modified habitats entail an ecological trap for Arabian babblers, which adjusts their life history traits as a response. Understanding the adaptation of social and cooperative breeding species to habitat modification is the first step for mitigating the processes behind human-related population declines.
PepPre: Promote Peptide Identification Using Accurate and Comprehensive Precursors
Authors: Tarn Ching , Wu Yu-Zhuo , Wang Kai-Fei 
Published: May 2023

AbstractAccurate and comprehensive peptide precursor ions are crucial to tandem mass spectrometry-based peptide identification. An identification engine can greatly benefit from the search space reduction hinted by credible and detailed precursors. Additionally, both the number of identifications and the spectrum explainability can be increased by considering multiple precursors per spectrum. Here, we propose PepPre, which detects precursors by decomposing peaks into multiple isotope clusters using linear programming methods. The detected precursors are scored and ranked, and the high-scoring ones are used for the following peptide identification. PepPre is evaluated both on regular and cross-linked peptides datasets, and compared with 11 methods in this paper. The experimental results show that PepPre achieves 203% more PSM and 68% more peptide identifications than instrument software for regular peptides, and 99% more PSM and 27% more peptide pair identifications for cross-linked peptides, which also outperforms all other evaluated methods. In addition to the increased identification numbers, further credibility evaluation evidence that the identifications are credible. Moreover, by widening the isolation window of data acquisition from 2 Th to 8 Th, the engine is able to identify at least 64% more PSMs with PepPre, demonstrating the potential advantages of large isolation windows.Graphical TOC Entry
Two distinct waves of transcriptome and translatome remodelling drive germline stem cell differentiation
Authors: Samuels Tamsin J. , Gui Jinghua , Gebert Daniel , Teixeira Felipe K. 
Published: May 2023

AbstractDevelopment and tissue maintenance require a supply of new cells, which are generated by division of stem cells and then specialised to perform their function. The differentiation process requires the tight control of gene expression, but this has been challenging to study in a systematic manner in adult stem cells, which are sparsely distributed in tissues. We have overcome these limitations by establishing a robust protocol to synchroniseDrosophilafemale germline stem cell (GSC) differentiationin vivo, allowing us to perform transcriptome and translatome analyses at high temporal resolution during differentiation. As well as observing dynamic changes in mRNA level, promoter usage and exon inclusion, our data reveal that changing translation efficiency is the predominant regulatory mechanism during GSC differentiation. Indeed, changes in mRNA level are frequently buffered by changes in translation efficiency, including the translational repression of transcripts that will only be translated later during oocyte maturation or embryo development. Contrary to the expected linear accumulation of changes from stem cell to differentiated cell, our data reveal that differentiation occurs through two distinct waves of changes in both the transcriptome and translatome level.
In Silico Investigations of Adaptive Therapy Using a Single Cytotoxic or a Single Cytostatic Drug
Authors: Saha Daniel K. , Anderson Alexander R. A. , Cisneros Luis , Maley Carlo C. 
Published: May 2023

AbstractAdaptive therapy, as per the dose modulation, dose-skipping, or intermittent treatment protocol works well for treatment using a single cytotoxic drug, under a wide range of scenarios and parameter settings. In contrast, adaptive therapy works well only under a limited number of scenarios and parameter settings when using a single cytostatic drug. In general, adaptive therapy works best under conditions of higher fitness cost, higher replacement rate, higher turnover. Adaptive therapy works best when drug dosages are changed as soon as a change in tumor burden is detected. In general, it is better to pause treatment sooner than later, when the tumor is shrinking If the amount of drug used is too low, it is unable to control the sensitive cells and the tumor grows. However, if the drug dose is too high, it quickly selects for resistant cells and eventually the tumor grows out of control. However, there appears to be intermediate levels of dosing, which we call the minimum effective dose, which is able to control the sensitive cells but is not high enough to select for the resistant cells to grow out of control.
Heterogeneity-Preserving Discriminative Feature Selection for Subtype Discovery
Authors: M. A. Basher Abdur Rahman , Hallinan Caleb , Lee Kwonmoo 
Published: May 2023

AbstractThe discovery of subtypes is pivotal for disease diagnosis and targeted therapy, considering the diverse responses of different cells or patients to specific treatments. Exploring the heterogeneity within disease or cell states provides insights into disease progression mechanisms and cell differentiation. The advent of high-throughput technologies has enabled the generation and analysis of various molecular data types, such as single-cell RNA-seq, proteomic, and imaging datasets, at large scales. While presenting opportunities for subtype discovery, these datasets pose challenges in finding relevant signatures due to their high dimensionality. Feature selection, a crucial step in the analysis pipeline, involves choosing signatures that reduce the feature size for more efficient downstream computational analysis. Numerous existing methods focus on selecting signatures that differentiate known diseases or cell states, yet they often fall short in identifying features that preserve heterogeneity and reveal subtypes. To identify features that can capture the diversity within each class while also maintaining the discrimination of known disease states, we employed deep metric learning-based feature embedding to conduct a detailed exploration of the statistical properties of features essential in preserving heterogeneity. Our analysis revealed that features with a significant difference in interquartile range (IQR) between classes possess crucial subtype information. Guided by this insight, we developed a robust statistical method, termed PHet (Preserving Heterogeneity) that performs iterative subsampling differential analysis of IQR and Fisher’s method between classes, identifying a minimal set of heterogeneity-preserving discriminative features to optimize subtype clustering quality. Validation using public single-cell RNA-seq and microarray datasets showcased PHet’s effectiveness in preserving sample heterogeneity while maintaining discrimination of known disease/cell states, surpassing the performance of previous outlier-based methods. Furthermore, analysis of a single-cell RNA-seq dataset from mouse tracheal epithelial cells revealed, through PHet-based features, the presence of two distinct basal cell subtypes undergoing differentiation toward a luminal secretory phenotype. Notably, one of these subtypes exhibited high expression of BPIFA1. Interestingly, previous studies have linked BPIFA1 secretion to the emergence of secretory cells during mucociliary differentiation of airway epithelial cells. PHet successfully pinpointed the basal cell subtype associated with this phenomenon, a distinction that pre-annotated markers and dispersion-based features failed to make due to their admixed feature expression profiles. These findings underscore the potential of our method to deepen our understanding of the mechanisms underlying diseases and cell differentiation and contribute significantly to personalized medicine.
TDbasedUFE and TDbasedUFEadv: bioconductor packages to perform tensor decomposition based unsupervised feature extraction
Authors: Taguchi Y-h. , Turki Turki 
Published: May 2023

AbstractMotivationTensor decomposition (TD) based unsupervised feature extraction (FE) was proposed almost five years ago and has been successfully applied to a wide range of bioinformatics problems ranging from biomarker identification to the identification of disease-causing genes and drug repositioning. Despite its successful applications, the use of TD-based unsupervised FE was not widely accepted because of the unpopularity of TD in this field.ResultsTo overcome this difficulty, we developed two bioconductor packages, TDbasedUFE and TDbasedUFEadv. Using these two packages, all researchers who are not familiar with the concept of TD can make use of TD-based unsupervised FE for their purposes. When the performances of two specific functions, identification of differentially expressed genes and multiomics analysis, are implemented in TDbasedUFE and compared with those of two state-of-the-art (SOTA) methods (i.e., DESeq2 and DIABLO), TDbasedUFE can outperform these two SOTAs.Availability and implementationTDbasedUFE and TDbasedUFEadv are freely available as R/Bioconductor packages hosted athttps://bioconductor.org/packages/TDbasedUFEandhttps://bioconductor.org/packages/TDbasedUFEadv, respectively.
Genome-wide CRISPRi knockdown to map gene essentiality landscape in coliphages λ and P1
Authors: Piya Denish , Nolan Nicholas , Moore Madeline L. , Ramirez Hernandez Luis A. , Cress Brady F. , Young Ry , Arkin Adam P. , Mutalik Vivek K. 
Published: May 2023

AbstractPhages are one of the key ecological drivers of microbial community dynamics, function and evolution. Despite their importance in bacterial ecology and evolutionary processes, phage genes are poorly characterized, hampering their usage in a variety of biotechnological applications. Methods to characterize such genes, even those critical to the phage life cycle, are labor-intensive and are generally phage-specific. Here, we develop a systematic gene essentiality mapping method scalable to new phage-host combinations that facilitate the identification of non-essential genes. As proof of concept, we use a catalytically inactive Cas12a mediated genome-wide CRISPRi assay to determine the essential genes in the canonical coliphages λ and P1. Results from a single panel of CRISPRi probes largely recapitulate the essential gene roster determined from decades of genetic analysis for lambda and provide new insights into essential and nonessential loci in P1. We present evidence of how CRISPRi polarity can lead to false positive gene essentiality assignments and recommend caution towards interpreting CRISPRi data on gene essentiality when applied to less studied phages. Finally, we show that we can engineer phages by inserting DNA barcodes into newly identified inessential regions, which will empower processes of identification, quantification and tracking of phages in diverse applications.
The Loss of the H1.4 Linker Histone Impacts Nascent Transcription and Chromatin Accessibility
Authors: Gokey Nolan G. , Ward James M. , Milliman Eric J. , Deterding Leesa J. , Trotter Kevin W. , Archer Trevor K. 
Published: May 2023

ABSTRACTThe Chromatosome superstructure, comprised of core histone containing nucleosomes and linker histones, act in concert as physical barriers to genetic material in the mammalian nucleus to trans-acting factors. Appropriate arrangement, composition, and post-translational modification of the chromatosome is highly regulated and necessary for appropriate gene expression. These proteins act to radically condense the genetic material and linker H1 histone is essential for the further condensation of the chromatin fiber. However, the regulatory role of H1 in gene expression and chromatin organization is complicated by cell type specific expression and compensation of multiple H1 variants. Leveraging the UL3 osteosarcoma cell line which displays biased expression of H1 variants, and CRISPR/Cas9, we generated H1.4-deficient clones. Loss of H1.4 results in consistent changes to chromatin accessibility concomitant with changes to histone tail modifications, as well as a set of differentially expressed genes shared among ΔH1.4 genetic clones. We identified immune and inflammation immediate early genes as enriched in differentially expressed genes, skewed towards AP-1 regulated targets. Our data show that H1.4 is critical for the regulation of stress response pathways.Key Points for NAR(3 bullet points summarizing the manuscript’s contribution to the field)H1.4 is essential for appropriate expression of over 6,000 nascent transcripts in UL3 cells.Loss of H1.4 results in widespread changes in chromatin accessibility at enhancers and transcribed regions as well as heterochromatin and quiescent chromatin.Immediate early genes, and especially AP-1 family members, are highly sensitive to H1.4 loss and their binding sites coincide with losses in chromatin accessibility