Analysis Of High-throughput Data Research Articles

Metagenomic Detection of Multiple Viruses in Monk Parakeet (Myiopsitta monachus) in Australia.

Birds are known to harbour many pathogens, including circovirus, herpesviruses, adenoviruses and Chlamydia psittaci. Some of these pose zoonotic risks, while others, such as beak and feather disease virus (BFDV), have a significant impact on the conservation of endangered bird species. This study was aimed to determine the faecal virome of a group of apparently healthy Monk parakeet using high-throughput sequencing. Fresh faecal samples were collected from four Monk parakeets at a pet shop in Melbourne, Australia. Virus enrichment and nucleic acid extraction were performed on the faecal samples, followed by high-throughput sequencing at the Australian Genome Research Facility (AGRF). Utilising an established pipeline for high-throughput sequencing data analysis, this study revealed the presence of three viruses of the families Circoviridae, Parvoviridae and Adenoviridae. Subsequent sequence comparison and phylogenetic analyses further confirmed that the detected viruses belong to the genera Chaphamaparvovirus (unassigned species), Circovirus (species Circovirus parrot) and Siadenovirus (species Siadenovirus viridis). Despite non-pathogenicity, the existence of multiple viruses within a bird species underscores the risk of these viruses spreading into the pet trade. Detection and a better understanding of avian viruses are crucial for the establishment of appropriate management and biosecurity measures in the domestic and international bird trade, which ultimately supports the conservation of vulnerable bird species.

Correction: High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

Correction: High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

High-throughput image data analysis shows the genetic diversity in Tartary buckwheat (Fagopyrum tataricum Gaertn.)

Rapid genome-wide profiling of DNA methylation and genetic variation using guide positioning sequencing (GPS).

Whole-genome bisulfite sequencing (WGBS) has been extensively utilized for DNA methylation profiling over the past decade. However, it has shown limitations in terms of high costs and inefficiencies. The productivity and accuracy of DNA methylation detection rely critically on the optimization of methodologies and the continuous refinements of related sequencing platforms. Here, we describe a detailed protocol of guide positioning sequencing (GPS), a bisulfite-based, location-specific sequencing technology designed for comprehensive DNA methylation characterization across the genome. The fundamental principle of GPS lies in the substitution of dCTP with 5-methyl-dCTP (5mC) at the 3'-end of DNA fragments by T4 DNA polymerase, which protects cytosines from bisulfite conversion to preserve the integrity of the base composition. This alteration allows the 3'-end to independently facilitate genetic variation profiling and guides the 5'-end, enriched with methylation information, to align more rapidly to the reference genome. Hence, GPS enables the concurrent detection of both genetic and epigenetic variations. Additionally, we provide an accessible description of the data processing, specifically involving certain software and scripts. Overall, the entire GPS procedure can be completed within a maximum of 15days, starting with a low initial DNA input of 100-500ng, followed by 4-5days for library construction, 8-10days for high-throughput sequencing (HTS) and data analysis, which can greatly facilitate the promotion and application of DNA methylation detection, especially for the rapid clinical diagnosis of diverse disease pathologies associated with concurrent genetic and epigenetic variations.

DrugReSC: targeting disease-critical cell subpopulations with single-cell transcriptomic data for drug repurposing in cancer.

The field of computational drug repurposing aims to uncover novel therapeutic applications for existing drugs through high-throughput data analysis. However, there is a scarcity of drug repurposing methods leveraging the cellular-level information provided by single-cell RNA sequencing data. To address this need, we propose DrugReSC, an innovative approach to drug repurposing utilizing single-cell RNA sequencing data, intending to target specific cell subpopulations critical to disease pathology. DrugReSC constructs a drug-by-cell matrix representing the transcriptional relationships between individual cells and drugs and utilizes permutation-based methods to assess drug contributions to cellular phenotypic changes. We demonstrate DrugReSC's superior performance compared to existing drug repurposing methods based on bulk or single-cell RNA sequencing data across multiple cancer case studies. In summary, DrugReSC offers a novel perspective on the utilization of single-cell sequencing data in drug repurposing methods, contributing to the advancement of precision medicine for cancer.

Decoding recurrent pregnancy loss: insights from comparative proteomics studies.

Recurrent pregnancy loss (RPL) represents a common disorder that affects up to 2% of the women aiming at childbirth with long-term consequences on family and society. Factors contributing to it in more than half of the cases are still unknown. Comparative proteomic analysis can provide new insights into the biological pathways underlining the pathogenesis of RPL. Until now, chorionic villi, decidua, placenta, endometrium, and maternal blood from women with RPL have been analyzed by comparative proteomics studies. In this review, we aimed to provide a critical evaluation of the published comparative studies of RPL on human samples, gathered by systematic literature search using PubMed and Google Scholar databases. We provide a detailed overview of the analyzed materials, proteomics platforms, proposed candidate biomarkers and altered pathways and processes linked with RPL. The top, most identified and validated biomarker candidates from all studies are discussed, followed by bioinformatics analysis of the available high-throughput data and presentation of common altered processes and pathways in RPL. Finally, future directions aimed at developing new and efficient therapeutic strategies are discussed as well.

Using Large Language Models for Microbiome Findings Reports in Laboratory Diagnostics

Background: Advancements in genomic technologies are rapidly evolving, with the potential to transform laboratory diagnostics by enabling high-throughput analysis of complex biological data, such as microbiome data. Large Language Models (LLMs) have shown significant promise in extracting actionable insights from vast datasets, but their application in generating microbiome findings reports with clinical interpretations and lifestyle recommendations has not been explored yet. Methods: This article introduces an innovative framework that utilizes LLMs to automate the generation of findings reports in the context of microbiome diagnostics. The proposed model integrates LLMs within an event-driven, workflow-based architecture, designed to enhance scalability and adaptability in clinical laboratory environments. Special focus is given to aligning the model with clinical standards and regulatory guidelines such as the In-Vitro Diagnostic Regulation (IVDR) and the guidelines published by the High-Level Expert Group on Artificial Intelligence (HLEG AI). The implementation of this model was demonstrated through a prototype called “MicroFlow”. Results: The implementation of MicroFlow indicates the viability of automating findings report generation using LLMs. Initial evaluation by laboratory expert users indicated that the integration of LLMs is promising, with the generated reports being plausible and useful, although further testing on real-world data is necessary to assess the model’s accuracy and reliability. Conclusions: This work presents a potential approach for using LLMs to support the generation of findings reports in microbiome diagnostics. While the initial results seem promising, further evaluation and refinement are needed to ensure the model’s effectiveness and adherence to clinical standards. Future efforts will focus on improvements based on feedback from laboratory experts and comprehensive testing on real patient data.

Impact of wheat processing on growth, serum biochemistry, and ruminal microbiota in sheep (Ovis aries)

This study investigated the impact of wheat processing methods (wheat flour vs wheat pellets) on the growth performance, serum biochemical parameters, and rumen microbiome composition in sheep. Results indicated that feeding of wheat flour resulted in significantly higher terminal weight and average daily gain (P < 0.05) and lower cholesterol and ALP04 levels (P < 0.05) in sheep compared to those fed wheat pellets. Analysis of 16s rDNA high-throughput sequencing data revealed significantly higher microbial richness (Chao1 index) in the rumen of sheep fed wheat flour (P < 0.05), even though the phylum-level composition dominated by Firmicutes, Bacteroidetes, and Proteobacteria was similar in both groups of sheep. Notably, sheep fed wheat flour were found to have a significantly higher relative abundance of Bacteroidetes (P < 0.05). At the genus level, Succinivibrionaceae_UCG-001 and Prevotella_1 were significantly more abundant in the rumen of sheep fed wheat flour (P < 0.05). Correlation analysis identified that both terminal weight and average daily gain were positively correlated with ruminal abundance of Bacteroidetes and Prevotella_1, while ALP04 was negatively correlated with the abundance of these taxa. Functional prediction using PICRUSt2 indicated enrichment of pathways related to the ABC-type glycerol-3-phosphate transport system, and periplasmic components in both wheat flour and pellet fed sheep. Overall, these findings suggest that dietary wheat flour modulates rumen microbiota composition, and improves growth performance in sheep.

Molecular mechanism of Rolupram reducing neuroinflammation in noise induced tinnitus mice through TLR4/NF kB/NLRP3 protein/caspase-1/IL-1 β signaling pathway

Noisy tinnitus is a common auditory system disease characterized by persistent tinnitus symptoms. The TLR4/NF - κ B/NLRP3 signaling pathway plays an important role in neuroinflammatory response. Select 6 control and 6 noise exposed mice for transcriptome sequencing analysis in the hippocampus, conduct high-throughput data analysis, identify differentially expressed genes, and screen for pathways. Auditory brainstem response (ABR) detection was performed to understand the hearing changes, and the modeling effect was evaluated using the GPIAS% inhibition experiment of auditory startle reflex. Morphological observation of the basement membrane was performed to determine whether the inner hair cells were damaged. Immunohistochemistry and immunofluorescence were used to determine the activation of microglia in the hippocampus of noise induced tinnitus mice. Finally, qPCR and Western Blot were used to detect the expression of TLR4, NF kB, NLRP3, caspase-1, and IL-1 β in the hippocampus of each group of mice. Through high-throughput data analysis, it was found that there was no significant difference in the auditory threshold of the three groups of mice; After 2 h of exposure to 100 dB SPL noise, the GPIAS% of mice decreased significantly compared to before exposure, and membrane construction was successful. After 7 days, the GPIAS% of the drug intervention group increased. After noise exposure, mice developed tinnitus, and hippocampus neuroinflammation. Roflupram can inhibit neuroinflammation and improve tinnitus through the TLR4/NF kB/NLRP3/caspase-1/IL-1 β signaling pathway.

Mapping the composite nature of clay matrix in mudstones: integrated micromechanics profiling by high-throughput nanoindentation and data analysis

AbstractMudstones and shales serve as natural barrier rocks in various geoenergy applications. Although many studies have investigated their mechanical properties, characterizing these parameters at the microscale remains challenging due to their fine-grained nature and susceptibility to microstructural damage introduced during sample preparation. This study aims to investigate the micromechanical properties of clay matrix composite in mudstones by combining high-speed nanoindentation mapping and machine learning data analysis. The nanoindentation approach effectively captured the heterogeneity in high-resolution mechanical property maps. Utilizing machine learning-based k-means clustering, the mechanical characteristics of matrix clay, brittle minerals, as well as measurements on grain boundaries and structural discontinuities (e.g., cracks) were successfully distinguished. The classification results were validated through correlation with broad ion beam-scanning electron microscopy images. The resulting average reduced elastic modulus (Er) and hardness (H) values for the clay matrix were determined to be 16.2 ± 6.2 and 0.5 ± 0.5 GPa, respectively, showing consistency across different test settings and indenter tips. Furthermore, the sensitivity of indentation measurements to various factors was investigated, revealing limited sensitivity to indentation depth and tip geometry (when comparing Cube corner and Berkovich tip in a small range of indentation depth variations), but decreased stability at lower loading rates. Box counting and bootstrapping methods were applied to assess the representativeness of parameters determined for the clay matrix. A relatively small dataset (indentation number = 60) is needed to achieve representativeness, while the main challenges is to cover a representative mapping area for clay matrix characterization. Overall, this study demonstrates the feasibility of high-speed nanoindentation mapping combined with data analysis for micromechanical characterization of the clay matrix in mudstones, paving the way for efficient analysis of similar fine-grained sedimentary rocks.

Comparison on the mechanism and potency of hepatotoxicity among hemp extract and its four major constituent cannabinoids

Cannabidiol (CBD) has been reported to induce hepatotoxicity in clinical trials and research studies; however, little is known about the safety of other nonintoxicating cannabinoids. New approach methodologies (NAMs) based on bioinformatic analysis of high-throughput transcriptomic data are gaining increasing importance in risk assessment and regulatory decision-making of data-poor chemicals. In the current study, we conducted a concentration response transcriptomic analysis of hemp extract and its four major constituent cannabinoids [CBD, cannabichromene (CBC), cannabigerol (CBG), and cannabinol (CBN)] in hepatocytes derived from human induced pluripotent stem cells (iPSCs). Each compound impacted a distinctive combination of biological functions and pathways. However, all the cannabinoids impaired liver metabolism and caused oxidative stress in the cells. Benchmark concentration (BMC) analysis showed potencies in transcriptional activity of the cannabinoids were in the order of CBN > CBD > CBC > CBG, consistent with the order of their cytotoxicity IC50 values. Patterns of transcriptomic changes induced by hemp extract and its median overall BMC were very similar to CBD but differed significantly from other cannabinoids, suggesting that potential adverse effects of hemp extract were largely due to its major constituent CBD. Lastly, transcriptomic point-of-departure (tPoD) values were determined for each of the compounds, with the value for CBD (0.106 µM) being concordant with a previously reported one derived from apical endpoints of clinical and animal studies. Taken together, the current study demonstrates the potential utility of transcriptomic BMC analysis as a NAM for hazard assessment of data-poor chemicals, improves our understanding of the possible health effects of hemp extract and its constituent cannabinoids, and provides important tPoD data that could contribute to inform human safety assessment of these cannabinoid compounds.

Custom Biomedical FAIR Data Analysis in the Cloud Using CAVATICA.

The historically fragmented biomedical data ecosystem has moved towards harmonization under the findable, accessible, interoperable, and reusable (FAIR) data principles, creating more opportunities for cloud-based research. This shift is especially opportune for scientists across diverse domains interested in implementing creative, nonstandard computational analytic pipelines on large and varied datasets. However, executing custom cloud analyses may present difficulties, particularly for investigators lacking advanced computational expertise. Here, we present an accessible, streamlined approach for the cloud compute platform CAVATICA that offers a solution. We outline how we developed a custom workflow in the cloud, for analyzing whole genome sequences of case-parent trios to detect sex-specific genetic effects on orofacial cleft risk, which required several programming languages and custom software packages. The approach involves just three components: Docker to containerize software environments, tool creation for each analysis step, and a visual workflow editor to weave the tools into a Common Workflow Language (CWL) pipeline. Our approach should be accessible to any investigator with basic computational skills, is readily extended to implement any scalable high-throughput biomedical data analysis in the cloud, and is applicable to other commonly used compute platforms such as BioData Catalyst. We believe our approach empowers versatile data reuse and promotes accelerated biomedical discovery in a time of substantial FAIR data.

Accumulation and ecological risk assessment of diazinon in surface sediments of Baiyangdian lake and its potential impact on probiotics and pathogens

Diazinon is an organophosphorus pesticide widely used in agriculture and household pest control, and its use also poses several environmental and health hazards. In this study, we investigated the spatial and temporal distribution of diazinon in Baiyangdian, evaluated its potential ecological risk and toxicity to aquatic organisms based on RQ (Risk quotient) and TU (Toxic unit) analysis, and assessed the potential effects of diazinon accumulation on probiotics and pathogens based on statistical analysis of high-throughput sequencing data. The results showed that diazinon in Baiyangdian posed a low to moderate chronic risk to sediment-dwelling organisms and a low toxicity effect on aquatic invertebrates, which was mainly concentrated in October and human-intensive areas. Meanwhile, increases in sediment electrical conductivity (EC), amorphous iron oxides content and phenol oxidase activity favored diazinon accumulation in sediments, whereas the opposite was the case for sediment organic carbon, β-1,4-glucosidase, phosphatase, catalase and pH, suggesting that environmental indicators play a key role in the behavior and distribution of diazinon. In addition, diazinon in heavily contaminated areas seem to inhibit the rare probiotics (Bifidobacterium adolescentis and Serratia sp.), while promoted dominant pathogens (e.g., Burkholderia cenocepacia), which can lead to increased disease risk to humans and ecosystems, disruption of ecological balance and potential health problems. However, probiotic Streptomyces xiamenensis resist to diazinon would be a potential degrader for diazinon remove. In conclusion, this study unveiled the effects of diazinon pollution on wetland ecosystems, emphasizing ecological impacts and potential health concerns. In addition, the discovery of diazinon resistant probiotics provided new insights into wetland ecological restoration.

APIR: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control.

Advances in mass spectrometry (MS) have enabled high-throughput analysis of proteomes in biological systems. The state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide-spectrum matches (PSMs), which convert mass spectra to peptide sequences. Different database search algorithms use distinct search strategies and thus may identify unique PSMs. However, no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate (FDR). To fill in this gap, we proposed a statistical framework, Aggregation of Peptide Identification Results (APIR), that is universally compatible with all database search algorithms. Notably, under an FDR threshold, APIR is guaranteed to identify at least as many, if not more, peptides as individual database search algorithms do. Evaluation of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the FDR. Real data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search algorithms. The APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis, e.g., differential gene expression analysis on RNA sequencing data. The APIR R package is available at https://github.com/yiling0210/APIR.

Metabolomics Characterization of Disease Markers in Diabetes and Its Associated Pathologies.

With the change in lifestyle of people, there has been a considerable increase in diabetes, which brings with it certain follow-up pathological conditions, which lead to a substantial medical burden. Identifying biomarkers that aid in screening, diagnosis, and prognosis of diabetes and its associated pathologies would help better patient management and facilitate a personalized treatment approach for prevention and treatment. With the advancement in techniques and technologies, metabolomics has emerged as an omics approach capable of large-scale high throughput data analysis and identifying and quantifying metabolites that provide an insight into the underlying mechanism of the disease and its progression. Diabetes and metabolomics keywords were searched in correspondence with the assigned keywords, including kidney, cardiovascular diseases and critical illness from PubMed and Scopus, from its inception to Dec 2023. The relevant studies from this search were extracted and included in the study. This review is focused on the biomarkers identified in diabetes, diabetic kidney disease, diabetes-related development of CVD, and its role in critical illness.

ASpedia-R: a package to retrieve junction-incorporating features and knowledge-based functions of human alternative splicing events.

Alternative splicing (AS) is a key regulatory mechanism that confers genetic diversity and phenotypic plasticity of human. The exons and their flanking regions include comprehensive junction-incorporating sequence features like splicing factor-binding sites and protein domains. These elements involve in exon usage and finally contribute to isoform-specific biological functions. Splicing-associated sequence features are involved in the multilayered regulation encompassing DNA and proteins. However, most analysis applications have investigated limited sequence features, like protein domains. It is insufficient to explain the comprehensive cause and effect of exon-specific biological processes. With the advent of RNA-seq technology, global AS event analysis has deduced more precise results. As accumulating analysis results, it could be a challenge to identify multi-omics sequence features for AS events. Therefore, application to investigate multi-omics sequence features is useful to scan critical evidence. ASpedia-R is an R package to interrogate junction-incorporating sequence features for human genes. Our database collected the heterogeneous profile encompassed from DNA to protein. Additionally, knowledge-based splicing genes were collected using text-mining to test the association with specific pathway terms. Our package retrieves AS events for high-throughput data analysis results via AS event ID converter. Finally, result profile could be visualized and saved to multiple formats: sequence feature result table, genome track figure, protein-protein interaction network, and gene set enrichment test result table. Our package is a convenient tool to understand global regulation mechanisms by splicing. The package source code is freely available to non-commercial users at https://github.com/ncc-bioinfo/ASpedia-R.

MYO5B levels are linked to microRNAs and UNC45A in colorectal cancer

Background: Colorectal cancer (CRC) is currently the third most prevalent cancer worldwide and ranks as the second deadliest. Colon cancer is characterized by disruption in cellular structure and polarity, processes that are commonly regulated by cell traffcking. The molecular motor Myosin 5b (Myo5b) is a key component of intracellular traffcking in the intestinal epithelium. Myo5b moves along F-actin tracks to transport cargo to and from the apical membrane. When Myo5b function is impaired, it leads to significant changes in gastrointestinal epithelial cells, including hyper-proliferation, alterations in tight junction proteins, and reduced differentiation. For Myo5b to function properly it must be properly folded; a process regulated by the myosin chaperone Unc45a. Recent work demonstrates that Myo5b levels are decreased in gastric cancer and during the progression of colorectal tumors. However, the mechanism driving this altered Myo5b expression in colorectal cancer remains unknown. We hypothesize that microRNAs regulate the expression of Unc45a, which subsequently impacts Myo5b function, promoting colorectal cancer. Methods and Results: Analysis of high-throughput RNA-sequencing data from the Cancer Genome Atlas (TCGA) revealed hyper-methylation of the Myo5b gene in CRC tumors (n=286) compared to controls (n=41). Findings in gastric cancer have shown that hyper-methylation leads to the silencing of Myo5b gene expression. Consistent with these findings, we observe a significant reduction in Myo5b gene expression in CRC tumors. Immunostaining of CRC tumor arrays and analysis from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) demonstrated a corresponding decrease in Myo5b protein levels. For comparison, we examined Myo5b levels in normal human colonic organoids and the colon cancer cell lines T84,HT-29, HT29-MTX, HCT-8 and HCT-116. Using qPCR, we confirmed a significant reduction in Myo5b gene expression in cancer cells compared to human colonic organoids. We postulated that even if Myo5b was expressed in CRC, it might not function properly if Unc45a function was perturbed. Using the same databases, we found that while Unc45a gene expression levels remain unchanged, its protein levels were significantly reduced. Analysis of the miRMap data reveled multiple microRNAs that could target Unc45a. The top 10 Unc45a-targeting microRNAs were all found to be elevated in CRC compared to normal control tissue, suggesting that microRNAs may lead to reduced Unc45a protein levels. The functional importance of decreased Myo5b in CRC was underscored by the observation that CRC patients with lower levels of Myo5b had reduced survival compared to those CRC patients with higher levels of Myo5b. Additionally, the loss of Myo5b correlated with increased ribosome biogenesis, a key driver of tumor proliferation. Conclusions: These findings indicate that reduced Myo5b in CRC may be attributed to gene methylation and impaired protein folding by Unc45a. The loss of Myo5b holds significance, as it is associated with poorer prognosis and linked with cancer progression. National Diabetes and Digestive and Kidney Diseases Grant R43 DK109820, RO1 DK048370, KO1 DK121869, MUSC start up funds, Histochemical 2022 Cornerstone Grant, and NIDDK T32. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

PAX1 represses canonical Wnt signaling pathway and plays dual roles during endoderm differentiation

BackgroundPaired box 1 (PAX1) is a transcription factor and essential for the development of pharyngeal pouches-derived tissues, including thymus. PAX1 mutations are identified in Severe Combined Immunodeficiency (SCID) patients with Otofaciocervical Syndrome Type 2 (OTFCS2). However, despite the critical roles of PAX1 in embryonic development and diseases, detailed insights into its molecular mode of action are critically missing.MethodsThe repressing roles of PAX1 and SCID associated mutants on Wnt signaling pathway were investigated by luciferase reporter assays, qRT-PCR and in situ hybridization in HEK293FT, HCT116 cells and zebrafish embryos, respectively. Co-immunoprecipitation (co-IP) and western blotting assays were carried out to identify the molecular mechanisms underlying PAX1’s role on Wnt signaling pathway. hESC based endoderm differentiation, flow cytometry, high-throughput sequencing data analysis, and qRT-PCR assays were utilized to determine the roles of PAX1 during endoderm differentiation.ResultsHere, we show that PAX1 represses canonical Wnt signaling pathway in vertebrate cells. Mechanically, PAX1 competes with SUMO E3 ligase PIASy to bind to TCF7L2, thus perturbing TCF7L2 SUMOylation level, further reducing its transcriptional activity and protein stability. Moreover, we reveal that PAX1 plays dual roles in hESC-derived definitive and foregut/pharyngeal endoderm cells, which give rise to the thymus epithelium, by inhibiting Wnt signaling. Importantly, our data show PAX1 mutations found in SCID patients significantly compromise the suppressing ability of PAX1 on Wnt signaling.ConclusionsOur study presents a novel molecular mode of action of PAX1 in regulation of canonical Wnt signaling and endoderm differentiation, thus providing insights for the molecular basis of PAX1 associated SCID, offering better understanding of the behavior of PAX1 in embryogenesis.

Unravelling the factors of evolutionary repeatability: insights and perspectives on predictability in evolutionary biology

Evolutionary biology was previously considered a historical science with predictions about evolutionary trajectories believed to be near impossible. The development of high throughput sequencing and data analysis technologies has challenged this belief, and provided an abundance of data that yields novel insights into evolutionary processes. Evolutionary predictions are now increasingly being used to develop fundamental knowledge of evolving systems and/or to demonstrate evolutionary control. Here we investigate the factors that make evolutionary repeatability more or less likely to increase the accuracy of evolutionary predictions. We identify outstanding questions and provide a potential starting point to determine how evolutionary repeatability is affected by genetic relatedness.

Tools and methods for high-throughput single-cell imaging with the mother machine

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, ‘what you put is what you get’ (WYPIWYG) – that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.