Biology subjects

Meyer, H. V.

Publications and source records attributed to Meyer, H. V..

11 recordsLinked to original sources

bioRxiv2026

gcat, its_a_scam: modular tools for single cell data processing and per-cellquality control

Motivation: The era of designing bioinformatic pipelines using open source tools is giving way to monolithic, and often proprietary, tools designed for end-to-end analysis, especially in the context of single cell data. While these tools are convenient for going from sequencing data to an almost complete data analysis "in a few clicks", they can strongly limit data use beyond standard analyses Results: We developed Gene and Chromosome Analysis Toolkit (gcat) and It's a scam To Sell Another Single Cell Analysis Method (its_a_scam) for modular processing and quality control of single cell genomic data, freeing users from their reliance on proprietary tools. gcat is a C++ library for parsing genomic data and working with it over genomic coordinates. its_a_scam is built on top of gcat and provides a suite of tools for a wide range of single cell data processing and quality control. its_a_scam can be used in conjunction with commonly used open source tools for scRNA-seq and scATAC-seq count matrix generation, and for performing per-cell quality control directly from alignment files. These alignment-level per-cell metrics can be used for quality control of each cell or to understand celltype differences post-clustering or post-annotation. Using a peripheral blood mononuclear cell Multiome dataset, we show that the count matrices generated with the aid of its_a_scam are highly concordant with those generated by proprietary pipelines. The alignment metrics further demonstrate celltype differences in aligned genomic regions, metagene profiles, and genebody coverage. The modular nature of its_a_scam allows users to integrate it into their existing pipelines, while also enabling analysis beyond current pipelines. Availability: gcat (distributed as gcatlib) and its_a_scam are open source and available at https://github.com/rishvanth-kp/gcatlib and https://github.com/rishvanth-kp/its_a_scam. Detailed documentation is available at https://gcatlib.readthedocs.io/ and https://its-a-scam.readthedocs.io.

bioinformatics↗

bioRxiv2026

Gene regulatory logic underlying the differentiation of mixed lineages in human thymic epithelial cells

The thymus enacts central tolerance through thymic (TECs), which present self-antigens to developing T cells self-reactive cells, preventing autoimmunity. Human TECs include populations that express lineage-committed programs corresponding to distinct peripheral tissues, yet how they acquire these programs and whether these TEC subpopulations are genetically related and conserved in mice remain unknown. Using matched single-cell gene-expression and chromatin-accessibility profiling of 126,687 human TECs, we identified 17 TEC subsets defined by their distinct expression profiles of 1,843 cell type restricted-antigens (CRAs) from 48 peripheral cell types. With a new phylogenetic reconstruction method, we show that lineage-committed TECs share a common ancestor, retain a thymus-specific mixed identity through co-expression of multiple CRA signatures, and are specified by coordinated activator and repressor regulons. We also identified a basement-membrane-collagen-rich state (BMC-TECs) with progenitor-like properties. Finally, mouse TECs shared the regulatory strategy but conserved few master transcription-factor drivers. This study mechanistically characterizes human TEC subpopulations and reveals the gene regulatory logic that leads to their distinct diversification and mixed identities in humans and mice.

immunology↗

bioRxiv2026

Dynamical consequences of context-dependent mutation: visualizing antibody sequence evolution during somatic hypermutation

Context-dependent mutation models describe how local sequence context affects individual substitution rates, but how these biases shape sequence evolution over multiple mutational steps remains challenging to characterize. Here, we develop a framework for analyzing and visualizing long-term sequence dynamics arising from context-dependent, non-reversible mutation processes. We apply it to somatic hypermutation during antibody affinity maturation, using previously inferred context-dependent mutation rates. Our analysis recovers conventional hypermutation hot- and cold-spot patterns while exposing substantial heterogeneity in mutability within these categories. We also identify sequences with low mutability that lack conventional cold-spot motifs. Applying the framework to antigen-binding regions from different antibody-encoding gene families reveals how differences in sequence composition lead to distinct mutational trajectories through sequence space, suggesting that hypermutation biases constrain how these regions diversify. We further show how mutational biases defined at the level of 5-nucleotide contexts propagate to the codon and amino acid levels, producing low stationary frequencies for stop codons and tyrosine and high stationary frequencies for proline. Changes in amino acid composition between naive and memory B cells are qualitatively consistent with these predictions. Together, our results provide a general framework for connecting local context-dependent mutation rates to long-term sequence evolution and demonstrate how intrinsic somatic hypermutation biases shape the relative accessibility of different regions of sequence space during affinity maturation.

evolutionary biology↗

bioRxiv2025

Sparse, random sampling is sufficient for central tolerance

Negative selection in the thymus limits autoimmunity by eliminating T cells that react strongly to self. Individual T cells, however, are only exposed to a small fraction of all self peptides during their "training" in the thymus, and it is puzzling how tolerance can be generalized to the remaining "test" self peptides across peripheral tissues in the body. Using a machine learning perspective, we show that such generalization is possible because the immune system satisfies two conditions: first that peptide abundance levels in the human thymus and periphery are highly correlated (i.e., training distribution {approx} test distribution), and second that cross-reactivity allows T cells to effectively learn binding information of similar peptides without explicitly interacting with all of them. Together, we show that sparse, random sampling of only 10% of self peptides in the thymus is sufficient to avoid reactivity to 90% of peripheral self, and we support this result with diverse experimental data. We then validate two predictions by our model; the first is that only 200-250 antigen presenting cells need to be seen by a T cell to ensure its robust selection, and the second relates how peptides missing from the thymus can drive auto-immunity of peripheral tissues. Overall, we provide a plausible answer to a long-standing question underlying adaptive immunity, and we highlight how generalization, a fundamental challenge faced by nearly every learning algorithm, is uniquely tackled by the immune system.

immunology↗

bioRxiv2025

plinkQC: An Integrated Tool for Ancestry Inference, Sample Selection, and Quality Control in Population Genetics

MotivationPopulation genetic analyses rely on high quality datasets that pass rigorous controls for sample and marker quality. Many analyses also require additional processing including identification of ancestry and sample relatedness. A software package that addresses all these common, yet crucial tasks is missing. ResultsWe have developed plinkQC, an R/CRAN package that combines these functionalities into a single software package with detailed vignettes for example applications. plinkQC determines the ancestry of study samples via a pre-trained random forest classifier that reaches 98% performance accuracy with just 5% of marker overlap between reference and user data. To obtain the maximal set of unrelated study samples, we developed a graph-based pruning method, taking both relationship estimates and sample quality into account. We demonstrate optimal sample selection on the 1000 Genomes project, where we retain an additional 71 samples compared to publicly available exclusion lists. Finally, plinkQC bundles these results together with per-individual and per-marker quality control checks into three simple functions and returns both the quality controlled data set and quality control report about each step of the analysis. AvailabilityplinkQC is available as an R/CRAN package. The documentation and code are available on github: https://meyer-lab-cshl.github.io/plinkQC/ and https://github.com/meyer-lab-cshl/plinkQC_manuscript.

bioinformatics↗

bioRxiv2024

BATMAN: Improved T cell receptor cross-reactivity prediction benchmarked on a comprehensive mutational scan database

Predicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database, called BATCAVE, encompassing complete single amino acid mutational assays of more than 22,000 TCR-peptide pairs, centered around 25 immunogenic human and mouse epitopes, across both major histocompatibility complex classes, against 151 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. We also developed an active learning version of BATMAN, which can efficiently learn the binding profile of a novel TCR by selecting an informative yet small number of peptides to assay. When validated on our database, BATMAN outperforms existing methods and reveals important biochemical predictors of TCR-peptide interactions. Finally, we demonstrate the broad applicability of BATMAN, including for predicting off-target effects for TCR-based therapies and polyclonal T cell responses.

immunology↗

bioRxiv2023

Unraveling the Phenotypic States of Human innate-like T Cells: Comparative Insights with Conventional T Cells and Mouse Models

The "innate-like" T cell compartment, known as Tinn, represents a diverse group of T cells that straddle the boundary between innate and adaptive immunity, having the ability to mount rapid responses following activation. In mice, this ability is acquired during thymic development. We explored the transcriptional landscape of Tinn compared to conventional T cells (Tconv) in the human thymus and blood using single cell RNA sequencing and flow cytometry. We reveal that in human blood, the majority of Tinn cells, including iNKT, MAIT, and V{delta}2+V{gamma}9+ T cells, share an effector program characterized by the expression of unique chemokine and cytokine receptors, and cytotoxic molecules. This program is driven by specific transcription factors, distinct from those governing Tconv cells. Conversely, only a fraction of thymic Tinn cells displays an effector phenotype, while others share transcriptional features with developing Tconv cells, indicating potential divergent developmental pathways. Unlike the mouse, human Tinn cells do not differentiate into multiple effector subsets but develop a mixed type I/type III effector potential. To conduct a comprehensive cross-species analysis, we constructed a murine Tinn developmental atlas and uncovered additional species-specific distinctions, including the absence of type II Tinn cells in humans, which implies distinct immune regulatory mechanisms across species. The study provides insights into the development and functionality of Tinn cells, emphasizing their role in immune responses and their potential as targets for therapeutic interventions.

immunology↗

bioRxiv2023

copepodTCR: Identification of Antigen-Specific T Cell Receptors with combinatorial peptide pooling

T cell receptor (TCR) repertoire diversity enables the antigen-specific immune responses against the vast space of possible pathogens. Identifying TCR-antigen binding pairs from the large TCR repertoire and antigen space is crucial for biomedical research. Here, we introduce copepodTCR, an open-access tool to design and interpret high-throughput experimental TCR specificity assays. copepodTCR implements a combinatorial peptide pooling scheme for efficient experimental testing of T cell responses against large overlapping peptide libraries, that can be used to identify the specificity of (or "deorphanize") TCRs. The scheme detects experimental errors and, coupled with a hierarchical Bayesian model for unbiased interpretation, identifies the response-eliciting peptide sequence for a TCR of interest out of hundreds of peptides tested using a simple experimental set-up. Using in silico simulations, we demonstrate the varied experimental settings in which copepodTCR yields efficient and interpretable TCR specificity results. We validated our approach on a library of 253 overlapping peptides covering the SARS-CoV-2 spike protein, split across 12 pools. A single stimulation with combinatorial pools identified the correct epitope of two TCRs with known specificity and then deorphanized two SARS-CoV-2 associated TCRs shared among a large cohort of COVID-19 patients. We provide experimental guides to efficiently design larger screens covering thousands of peptides which will be crucial to identify antigen-specific T cells and their targets from limited clinical material.

immunology↗

bioRxiv2022

Snakeobjects: an object-oriented workflow management system

Snakemake is one of the most popular workflow management systems, particularly in biological sciences. Snakemake workflows are highly portable, scalable, and transparent. Moreover, they enable the painless reproduction of published results and adaption to similar data processing and analysis projects. Here we present Snakeobjects, an extension of Snakemake that considerably simplifies the development of workflows and improves their readability and adaptability while preserving all the benefits of Snakemake that ensure the large and growing community of its users.

bioinformatics↗

bioRxiv2022

CRISPR-induced exon skipping of β-catenin reveals tumorigenic mutants driving distinct subtypes of liver cancer

CRISPR/Cas9-driven cancer modeling studies are based on disruption of tumor suppressor genes (TSGs) by small insertions or deletions (indels) that lead to frame-shift mutations. In addition, CRISPR/Cas9 is widely used to define the significance of cancer oncogenes and genetic dependencies in loss-of function studies. However, how CRISPR/Cas9 influences gain-of-function oncogenic mutations is elusive. Here, we demonstrate that single guide RNA targeting exon 3 of {beta}-catenin results in exon skipping and generates gain-of-function isoforms in vivo. CRISPR/Cas9-mediated exon skipping of {beta}-catenin induces liver tumor formation in synergy with YAPS127A in mice. We define two distinct exon skipping-induced tumor subtypes with different histological and transcriptional features. Notably, ectopic expression of two exon-skipped {beta}-catenin transcript isoforms together with YAPS127A phenocopies the two distinct subtypes of liver cancer. Moreover, we identify similar {beta}-catenin exon skipping events in hepatocellular carcinoma (HCC) patients. Collectively, our findings advance our understanding of {beta}-catenin-related tumorigenesis and reveal that CRISPR/Cas9 can be repurposed, in vivo, to study gain-of-function mutations of oncogenes in cancer.

cancer biology↗

bioRxiv2019

Genomic analysis reveals a functional role for myocardial trabeculae in adults

Since being first described by Leonardo da Vinci in 1513 it has remained an enigma why the endocardial surfaces of the adult heart retain a complex network of muscular trabeculae - with their persistence thought to be a vestige of embryonic development. For causative physiological inference we harness population genomics, image-based intermediate phenotyping and in silico modelling to determine the effect of this complex cardiovascular trait on function. Using deep learning-based image analysis we identified genetic associations with trabecular complexity in 18,097 UK Biobank participants which were replicated in an independently measured cohort of 1,129 healthy adults. Genes in these associated regions are enriched for expression in the fetal heart or vasculature and implicate loci associated with haemodynamic phenotypes and developmental pathways. A causal relationship between increasing trabecular complexity and both ventricular performance and electrical activity are supported by complementary biomechanical simulations and Mendelian randomisation studies. These findings show that myocardial trabeculae are a previously-unrecognised determinant of cardiovascular physiology in adult humans.

genomics↗