Biology subjects

Riffle, M.

Publications and source records attributed to Riffle, M..

At least 19 recordsLinked to original sources

bioRxiv2026

The Findings Workflow: An agentic, collaborative, provenance-tracked framework for scientific data analysis with Claude Code

Scientists presented with quantitative data often face two related problems: confidence that an analysis is statistically and computationally correct, and provenance sufficient to reproduce the resulting figures and conclusions. Large language models (LLMs) can make sophisticated analyses accessible through a collaborative, conversational interface, but unstructured use can worsen both problems by producing plausible but incorrect results and obscuring the steps that produced them. Here we present the Findings Workflow, a free, open-source Claude Code plugin designed to retain the flexibility of conversational LLM-assisted analysis while placing it within a structured, verifiable, and reproducible environment. The workflow uses staged, gated progression to ensure the science, metadata, and data are understood and tested data loaders are built before downstream analysis begins. Specialized subagents perform statistical analysis, programming, visualization, research, reporting, and validation, with independent review incorporated throughout. The plugin also provides vetted analysis templates, conventions, and methodological guidance that serve as trusted sources of opinion when deciding how analyses should be performed. Scientists remain actively involved throughout, exploring their data and directing the analysis through natural-language conversation, while the workflow supplies structure, verification, and persistent state around that collaboration. Findings are recorded automatically together with the data, code, methods, figures, and provenance needed to reproduce them. We demonstrate the workflow with a worked example that reproduces central findings from a published plasma extracellular vesicle proteomics dataset examining Alzheimer's disease (Mag-Net). Together, these features are designed to make LLM-assisted scientific analysis easier to trust, inspect, and reproduce without sacrificing its conversational and exploratory nature.

bioinformatics↗

bioRxiv2026

CasanovoGUI: a cross-platform desktop application for deep learning-based de novo peptide sequencing with Casanovo

De novo peptide sequencing detects peptides directly from tandem mass spectra without a protein sequence database, and deep learning has substantially advanced its performance. Casanovo, one such widely used model, is distributed as a Python command-line program. Consequently, installation, GPU and dependency configuration, and manual parameterization can be challenging for many bench scientists and are a recurring source of errors. Interpreting and validating the resulting predictions poses a further challenge. We present CasanovoGUI, an open-source Java-based desktop application that makes all of Casanovo's main analysis functions available through a point-and-click interface on Windows, macOS, and Linux. On first use, CasanovoGUI automatically installs a private Python environment and Casanovo with a GPU-matched build, requiring no prior software setup. The GUI provides access to Casanovo's analysis functions and configuration parameters, streams live progress, and integrates results interpretation: annotated spectra with per-residue confidence scores in the PDV viewer, and mismatch-tolerant mapping of de novo peptides back to a reference proteome. CasanovoGUI is available at https://github.com/Noble-Lab/CasanovoGUI.

bioinformatics↗

bioRxiv2026

Circulating extracellular vesicles in plasma carry accessible molecular signatures of aging in mice

Cells release membrane-bound extracellular vesicles into the bloodstream laden with proteins that may reflect their physiological state. How this circulating EV proteome changes across life remains poorly understood. Identifying molecular signatures of aging in accessible biofluids could facilitate earlier intervention and monitoring of age-related disease. Many circulating aging proteome studies rely on affinity-based platforms which suffer from poor cross-species translation, ambiguous signal attribution, and inconsistent agreement between platforms. Here, we present a characterization of the aging plasma EV proteome from a cross-sectional cohort of 86 male and female C57BL/6J mice (5-31 months). We leveraged a species-agnostic EV enrichment (Mag-Net) and mass spectrometry to detect 2,575 protein groups from 15,969 peptides. Protein abundance heterogeneity increased with age and the abundance of 272 proteins were significantly correlated with chronological age including established senescence and frailty markers. Proteins increasing with age were enriched in genome maintenance pathways, while those decreasing were associated with the extracellular matrix organization and lipid metabolism. Notably, several of the strongest age-increased proteins converged on Alzheimers and Parkinsons disease pathology. We observed sexual divergence in the aging EV proteome not previously characterized at this resolution. A proteomic clock built from this data accurately predicts chronological age, and peptide-level analysis reveals aging signals invisible at protein-level. These findings demonstrate that EV-enriched plasma proteomics can identify known aging markers, reveal novel sex-specific age-related changes, and generate predictive models of chronological age. This study provides a species-agnostic foundation for proteomic clocks that complement epigenetic approaches to monitor aging and evaluate healthspan.

molecular biology↗

bioRxiv2026

Spatiotemporal Mapping of Phosphorylation and Oxidation in the Pig Lens

BackgroundThe developmental pattern of the crystalline lens provides a unique model to study biological aging and its effects on the posttranslational modification of long-lived proteins. The orderly differentiation of lens fiber cells leads to a spatiotemporal gradient where mature, organelle-free fiber cells are packed in the lens nucleus surrounded by con-centric rings of successively younger fiber cells in the cortex. MethodsPig lenses were separated into six layers by dissolution in a hypotonic buffer. The changes in protein abundance, oxidation, and phosphorylation that occur across the spatiotemporal gradient of the lens were assessed quantitatively by using data independent acquisition label-free proteomic analysis of these six fractions. ResultsExpected changes in protein abundance of major lens protein which reflect the maturation process of lens fiber cells across the spatiotemporal gradient were found. Significant differences were noted in phosphorylation sites on crystallins, phakinin, and actin. Significant changes in oxidation of residues on essential lens proteins, as well as several glycolytic enzymes, were found across the spatiotemporal gradient. ConclusionDissolution of the lens followed by high resolution data-independent acquisition proteomics is a powerful technique for spatial mapping of protein abundance and posttranslational modification changes in the lens. The oxidation and phosphorylation sites noted in this study may play important roles in both lens development and cataractogenesis.

Molecular Biology↗

bioRxiv2026

A quantitative proteomics dataset for assessment and prediction of low dose X-ray radiation exposure in mice.

Ionizing radiation induces molecular responses that may be used to estimate exposure when physical dosimeters are unavailable. Here we present two large-scale proteomics datasets generated from mouse dorsal skin punch samples collected following controlled X-ray exposures spanning multiple doses, dose rates, and post-exposure time points. Experiment 1 comprised 96 samples (including 16 reference samples) collected 6 days after exposure to 0-75 cGy delivered at either 30 or 300 cGy/min. Experiment 2 comprised 936 samples (including 236 reference samples) exposed to 0-100 cGy at either 3 or 28 cGy/min dose rates and harvested between 7 and 150 days post-exposure. All samples were processed using a standardized workflow involving automated bead-based digestion and data-independent acquisition mass spectrometry. The datasets include multiple pooled reference sample types, process controls, and system suitability standards ensuring high quality data. All data presented are available via ProteomeXchange at several levels of processing, from raw files through normalized peptide- and protein-level abundance matrices suitable for biomarker discovery and machine learning applications. This dataset will facilitate generation of new insights into the biological changes and molecular signatures resulting from X-ray exposure in mice and may also help inform future studies in humans.

biochemistry↗

bioRxiv2026

Multimodal analysis of molecular remodeling in aging spleen identified global and cell type specific changes

Aging reshapes the cellular and molecular landscape of mammalian tissues. These changes can be progressive, preceding linearly with age, or occur as abrupt transitions of the course of lifespan. To investigate the age-dependent cellular and molecular shifts we profiled matched proteomes and transcriptomes from male and female murine spleens across eight time points, from stable adults through late life. The spleen was chosen to integrate understanding of age-dependent changes associated with immune surveillance, inflammaging, and immune-related proteostasis. Male and female mice follow distinct aging trajectories particularly in protein-RNA correlation in late life, reflecting both compositional shifts and failure of post-transcriptional buffering. To investigate whether these changes could be attributed to specific cell-types within the spleen, we developed Celestial, a machine-learning framework to identify cell-type-specific changes in bulk tissue samples. We found that age-related bulk molecular changes could be attributed in part to compositional remodeling of cell-types--expansion of GZMK+ CD8+ T cells and C1Q+ macrophages alongside naive T cell and global B cell loss. These results demonstrate that cell-type-aware interpretation can inform bulk multi-omic data for accurate mechanistic inference in heterogeneous tissues undergoing complex molecular remodeling.

systems biology↗

bioRxiv2026

Is Protein Quantification and Physical Normalization Always Necessary in Proteomics?

Dogma suggests protein quantification is a pre-requisite to LC-MS/MS based proteomics studies. Such quantification allows a standardized ratio of sample to digestion enzyme and enables physical normalization of protein digest loaded onto the mass spectrometer for analysis. Most proteomics studies include these steps. However, there are significant costs in time, money and experimental complexity, associated with performing protein quantification and physical normalization for every sample, especially for larger studies. Proteomics data analysis pipelines typically include computational normalization strategies to compensate for unavoidable systematic biases. These strategies also have the potential to compensate for avoidable variation such as omitting sample amount normalization. Here we investigate the effects of either physically normalizing the amount of protein for each individual sample or leaving it unnormalized. Our results show the relationship between increased protein amount variation in sample input, and the variance of quantified relative abundances of peptides and proteins output after data analysis. The experiments presented here suggest that protein quantification and physical normalization steps can be omitted from some quantitative proteomic experiments without incurring an unacceptable increase in measurement variability after computational normalization has been applied. This work will enable important time and cost saving optimizations to be made to many proteomics workflows.

biochemistry↗

bioRxiv2026

Proteomic Profiling of Human Omental and Subcutaneous Adipose Tissue in Individuals with a Broad Range of BMI.

Obesity is a major public health challenge affecting an ever-increasing proportion of the global population. It is associated with numerous comorbidities. Progressive expansion and remodeling of adipose tissue may lead to depot specific changes in adipose tissue biology and energy partitioning. Such changes likely precede the development of obesity-related complications. To facilitate a deeper understanding of adipose tissue biology, a comprehensive and quantitative proteomic dataset at the peptide and protein level is presented. Data-independent acquisition LC-MS/MS data were acquired from matched subcutaneous and omental adipose tissues from metabolically healthy individuals with no comorbidities and covering a wide range of body mass indexes. Adipose tissue samples were collected during elective surgeries and immediately processed for histology or frozen until proteomic analysis. Internal and external quality control systems ensured high quality data. All data presented are available via ProteomeXchange. This dataset will allow new insights into biological changes that evolve with increasing adiposity captured before the onset of comorbidities. Matched sampling across fat depots provides an opportunity to uncover depot-specific physiological signatures.

systems biology↗

bioRxiv2025

Improvements to Casanovo, a deep learning de novo peptidesequencer

Casanovo is a state-of-the-art deep learning model for de novo peptide sequencing from mass spectrometry proteomics data. Here we report on a series of enhancements to Casanovo, aimed at improving the interpretability of the scores assigned to predicted peptides, generalizing the software for use in database search, speeding up training and prediction runtimes, and providing workflows and visualization tools to facilitate adoption of Casanovo and interpretation of its results. Our goal is to make Casanovo accurate and easy to use for applications such as metaproteomics, antibody sequencing, immunopeptidomics, and discovery of novel peptide sequences in standard proteomics analyses. Casanovo is available as open source at https://github.com/Noble-Lab/casanovo.

molecular biology↗

bioRxiv2025

Improved quantitative accuracy in data-independent acquisition proteomics via retention time boundary imputation

The traditional approaches to handling missing values in data-independent acquisition (DIA) proteomics are to either remove high-missingness proteins or impute missing values with statistical procedures. Both approaches have their disadvantages: removal can limit statistical power, whereas imputation can introduce spurious correlations or dilute signal. We present an alternative approach based on imputing peptide retention times (RTs) rather than quantitations. For each missing value, we impute the start and end of the peptides RT profile ("RT boundaries"), then obtain a quantitative value by integrating the chromatographic signal within the imputed boundaries. We evaluate our method on three distinct datasets and show that RT boundary imputation produces more accurate quantitations than traditional imputation methods and reduces peptide lower limit of quantitation. Additionally, RT boundary imputation allows quantitative ratios to be obtained between experimental groups in cases where this would otherwise not be possible, as demonstrated with matrix matched calibration curve and Alzheimers disease datasets. Finally, we show that RT boundary imputation improves the ability to estimate radiation exposure in biological tissues. Our RT boundary imputation method, called Nettle, is available as a standalone tool with an open-source software license.

bioinformatics↗

bioRxiv2025

The E3 ubiquitin ligase MGRN1 targets melanocortin receptors MC1R and MC4R via interactions with transmembrane adapters

E3 ubiquitin ligases play a crucial role in modulating receptor stability and signaling at the cell surface, yet the mechanisms governing their substrate specificity remain incompletely understood. Mahogunin Ring Finger 1 (MGRN1) is a membrane-tethered E3 ligase that fine-tunes signaling sensitivity by targeting surface receptors for ubiquitination and degradation. Unlike cytosolic E3 ligases, membrane-tethered E3s require transmembrane adapters to selectively recognize and regulate surface receptors, yet few such ligases have been studied in detail. While MGRN1 is known to regulate the receptor Smoothened (SMO) within the Hedgehog pathway through its interaction with the transmembrane adapter Multiple Epidermal Growth Factor-like 8 (MEGF8), the broader scope of its regulatory network has been speculative. Here, we identify Attractin (ATRN) and Attractin-like 1 (ATRNL1) as additional transmembrane adapters that recruit MGRN1 and regulate cell surface receptor turnover. Through co-immunoprecipitation, we show that ATRN and ATRNL1 likely interact with the RING domain of MGRN1. Functional assays reveal that MGRN1 requires these transmembrane adapters to ubiquitinate and degrade the melanocortin receptors MC1R and MC4R, in a process analogous to its regulation of SMO. Loss of MGRN1 leads to increased surface and ciliary localization of MC4R in fibroblasts and elevated MC1R levels in melanocytes, with the latter resulting in enhanced eumelanin production. These findings expand the repertoire of MGRN1-regulated receptors and provide new insight into a shared mechanism by which membrane-tethered E3 ligases utilize transmembrane adapters to dictate substrate receptor specificity. By elucidating how MGRN1 selectively engages with surface receptors, this work establishes a broader framework for understanding how this unique class of E3 ligases fine-tunes receptor homeostasis and signaling output.

cell biology↗

bioRxiv2024

Force transmission through the inner kinetochore is enhanced by centromeric DNA sequences

Previously, we reconstituted a minimal functional kinetochore from recombinant Saccharomyces cerevisiae proteins that was capable of transmitting force from dynamic microtubules to nucleosomes containing the centromere-specific histone variant Cse4 (Hamilton et al. 2020). This work revealed two paths of force transmission through the inner kinetochore: through Mif2 and through the Okp1/Ame1 complex (OA). Here, using a chimeric DNA sequence that contains crucial centromere-determining elements of the budding yeast point centromere, we demonstrate that the presence of centromeric DNA sequences in Cse4-containing nucleosomes significantly strengthens OA-mediated linkages. Our findings indicate that centromeric sequences are important for the transmission of microtubule-based forces to the chromosome.

biochemistry↗

bioRxiv2024

Limelight - An open, web-based tool for visualizing, sharing, and analyzing mass spectrometry data from DDA pipelines

Liquid chromatography-tandem mass spectrometry employing data-dependent acquisition (DDA) is a mature, widely used proteomics technique routinely applied to proteome profiling, protein-protein interaction studies, biomarker discovery, and protein modification analysis. Numerous tools exist for searching DDA data and myriad file formats are output as results. While some search and post processing tools include data visualization features to aid biological interpretation, they are often limited or tied to specific software pipelines. This restricts the accessibility, sharing and interpretation of data, and hinders comparison of results between different software pipelines. We developed Limelight, an easy-to-use, open-source, freely available tool that provides data sharing, analysis and visualization and is not tied to any specific software pipeline. Limelight is a data visualization tool specifically designed to provide access to the whole "data stack", from raw and annotated scan data to peptide-spectrum matches, quality control, peptides, proteins, and modifications. Limelight is designed from the ground up for sharing and collaboration and to support data from any DDA workflow. We provide tools to import data from many widely used open-mass and closed-mass search software workflows. Limelight helps maximize the utility of data by providing an easy-to-use interface for finding and interpreting data, all using the native scores from respective workflows.

bioinformatics↗

bioRxiv2024

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics

Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we developed Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models. To make Carafe more accessible to the community, we have integrated Carafe into the widely used Skyline tool.

bioinformatics↗

bioRxiv2024

Microbial Metagenomes Across a Complete Phytoplankton Bloom Cycle:High-Resolution Sampling Every 4 Hours Over 22 Days

In May and June of 2021, marine microbial samples were collected for DNA sequencing in East Sound, WA, USA every 4 hours for 22 days. This high temporal resolution sampling effort captured the last 3 days of a Rhizosolenia sp. bloom, the initiation and complete bloom cycle of Chaetoceros socialis (8 days), and the following bacterial bloom (2 days). Metagenomes were completed on the time series, and the dataset includes 128 size-fractionated microbial samples (0.22-1.2 {micro}m), providing gene abundances for the dominant members of bacteria, archaea, and viruses. This dataset also has time-matched nutrient analyses, flow cytometry data, and physical parameters of the environment at a single point of sampling within a coastal ecosystem that experiences regular bloom events, facilitating a range of modeling efforts that can be leveraged to understand microbial community structure and their influences on the growth, maintenance, and senescence of phytoplankton blooms.

ecology↗

bioRxiv2024

A framework for quality control in quantitative proteomics

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and on ProteomeXchange under the identifier PXD051318.

biochemistry↗

bioRxiv2024

Resilience in a time of stress: revealing the molecular underpinnings of coral survival following thermal bleaching events

Coral bleaching events from thermal stress are increasing globally in duration, frequency, and intensity. Bleaching occurs when a corals algal symbionts are expelled, resulting in a loss of color. Some coral colonies survive bleaching, reacquire their symbionts and recover. In this study, we experimentally bleached Montipora capitata colonies to examine molecular and physiological signatures of intrinsic differences between corals that recover (resilient) compared to those that die (susceptible). All colonies were collected from the same bay and monitored for eight months post-bleaching to identify specific colonies exhibiting long-term resilience and survival. Using an integrated systems-biology approach that included quantitative mass spectrometry-based proteomics, 16S rRNA of the microbiome, total lipids, symbiont density and diversity, we explored molecular-level mechanisms of tolerance in pre- and post-bleached colonies and found biomarkers of resilience that can confidently identify resilient and susceptible corals before thermal-induced bleaching events. Prior to thermal stress, resilient corals were characterized by a more diverse microbiome and increased abundances of proteins involved in multiple carbon and nitrogen acquisition strategies, symbiont retention and acquisition, and pathogen resistance. Susceptible corals had early signs of symbiont rejection and had resorted to utilizing urea uptake pathways for carbon and nitrogen. Further, molecular signatures identified prior to bleaching were amplified after bleaching, suggesting these pathways may be deterministic in a colonys fate. Our results have important implications for the future of reefs, revealing molecular factors necessary for survival through thermally-induced bleaching events and providing diagnostic biomarkers for coral reef management. Significance statementCorals are being negatively impacted by the increase in the number and duration of thermal-induced bleaching events. There are, however, some individuals within a single species that will bleach and, after time, reacquire symbionts and physiologically recover while neighboring colonies will die. Here, we used a multidisciplinary approach to understand the biochemical details of the physiological changes of resilient and susceptible Montipora capitata to thermal-induced bleaching. Resilient corals were characterized by their use of multiple carbon and nitrogen acquisition strategies, metabolically active symbiont relationships, abundant antiviral proteins, and a diverse microbiome. We reveal a multi-factor molecular-level approach for confidently identifying resilient and susceptible coral colonies so that environmental managers can rapidly select quality candidates for propagation while in the field.

ecology↗

bioRxiv2024

A Workflow for Improved Analysis of Cross-linking Mass Spectrometry Data Integrating Parallel Accumulation-Serial Fragmentation with MeroX and Skyline

Cross-linking mass spectrometry (XL-MS) has evolved into a pivotal technique for probing protein interactions. This study describes the implementation of Parallel Accumulation-Serial Fragmentation (PASEF) on timsTOF instruments, enhancing the detection and analysis of protein interactions by XL-MS. Addressing the challenges in XL-MS, such as the interpretation of complex spectra, low abundant cross-linked peptides, and a data acquisition bias, our current study integrates a peptide-centric approach for the analysis of XL-MS data and presents the foundation for integrating data-independent acquisition (DIA) in XL-MS with a vendor-neutral and open-source platform. A novel workflow is described for processing data-dependent analysis (DDA) of PASEF-derived information. For this, software by Bruker Daltonics is used, enabling the conversion of these data into a format that is compatible with MeroX and Skyline software tools. Our approach significantly improves the identification of cross-linked products from complex mixtures, allowing the XL-MS community to overcome current analytical limitations.

bioinformatics↗