PLoS Computational Biology最新文献

A fundamental problem of visuomotor adaptation research is to understand how the brain is capable to asymptotically remove a predictable exogenous disturbance from a visual error signal using limited sensor information by re-calibration of hand movement. From a control theory perspective, the most striking aspect of this problem is that it falls squarely in the realm of the internal model principle of control theory. Despite this fact, the relationship between the internal model principle and models of visuomotor adaptation is currently not well developed. This paper aims to close this gap by proposing an abstract discrete-time state space model of visuomotor adaptation based on the internal model principle. The proposed DO Model, a metonym for its most important component, a disturbance observer, addresses key modeling requirements: modular architecture, physically relevant signals, parameters tied to atomic behaviors, and capacity for abstraction. The two main computational modules are a disturbance observer, a recently developed class of internal models, and a feedforward system that learns from the disturbance observer to improve feedforward motor commands.

Phase resetting of cardiac oscillators underlies some complex arrhythmias. Here we use optogenetic stimulation to construct phase response curves (PRC) for spheroids of human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CM) and a computational cardiomyocyte model to identify ionic mechanisms shaping the PRC. The clinical utility of the human PRCs is demonstrated by adding a patient-based conduction delay to the same equations to explain complex multi-day Holter ECG dynamics and cardiac arrhythmias. Periodic stimulation of these patient-based models and the computational model of human iPSC-CM reveal similar bifurcation patterns and entrainment zones. Cell therapy by injecting iPSC-CM into diseased hearts can induce ectopic foci-based engraftment arrhythmias. The PRC analysis offers a potential strategy to entrain these foci in a parameter space that avoids such arrhythmias.

Long-read sequencing has transformed many areas of biology and holds significant promise for telomere research by enabling analysis of nucleotide-level resolution chromosome arm-specific telomere length in both model organisms and humans. However, the adoption of new technologies, particularly in clinical or diagnostic contexts, requires careful validation to recognize potential technical and computational limitations. We present TARPON (Telomere Analysis and Research Pipeline Optimized for Nanopore), a best-practices Nextflow pipeline designed for the analysis of telomeres sequenced on the Oxford Nanopore Technologies (ONT) platform. TARPON can be executed via the command line or integrated into ONT's EPI2ME agent, providing a user-friendly graphical interface for those without computational training. Nextflow's container-based architecture eliminates dependency conflicts, thereby streamlining deployment across platforms. TARPON isolates telomeric repeat-containing reads, assigns strand specificity, and identifies enrichment probes that can be used both for demultiplexing and for confirming capture-based library preparation. To ensure that the analysis is restricted to full-length telomeres, reads lacking a capture probe or non-telomeric sequence on the opposite end are excluded. A sliding-window approach defines the subtelomere-to-telomere boundary, followed by quality filtering to remove low-quality or subtelomeric reads that passed earlier steps. The pipeline generates customizable statistics, text-based summaries, and publication-ready visualizations (HTML, PNG, PDF). While default settings are optimized for diagnostic workflows, all parameters are easily adjustable via the GUI or command line to support diverse applications. These include telomere analyses in variant-rich samples (e.g., ALT-positive tumors) and organisms with non-canonical telomeric repeats such as some insects (GTTAG) and certain plants (GGTTTAG). TARPON is the first complete and experimentally validated pipeline for Nanopore-based telomere analysis requiring no data pre-processing or prior bioinformatics expertise, while offering flexibility for advanced users.

Motivation: Understanding how different cell types interact spatially within tissue microenvironments is critical for deciphering immune dynamics, tumor progression, and tissue organization. Many current spatial analysis methods assume symmetric associations or compute image-level summaries separately without sharing information across patients and cohorts, limiting biological interpretability and statistical power.

Results: We present SHADE (Spatial Hierarchical Asymmetry via Directional Estimation), a multilevel Bayesian framework for modeling asymmetric spatial interactions across scales. SHADE quantifies direction-specific cell-cell associations using smooth spatial interaction curves (SICs) and integrates data across tissue sections, patients, and cohorts. Through simulation studies, SHADE demonstrates improved accuracy, robustness, and interpretability over existing methods. Application to colorectal cancer multiplexed imaging data demonstrates SHADE's ability to quantify directional spatial patterns while controlling for tissue architecture confounders and capturing substantial patient-level heterogeneity. The framework successfully identifies biologically interpretable spatial organization patterns, revealing that local microenvironmental structure varies considerably across patients within molecular subtypes.

The integration of single-cell multi-omics data provides a powerful approach for understanding the complex interplay between different molecular modalities, such as RNA expression, chromatin accessibility and protein abundance, measured through assays like scRNA-seq, scATAC-seq and CITE-seq, at single-cell resolution. However, most existing single-cell technologies focus on individual modalities, limiting a comprehensive understanding of their interconnections. Integrating such diverse and often unpaired datasets remains a challenging task due to unknown cell correspondences across distinct feature spaces and limited insights into cell-type-specific activities in non-scRNA-seq modalities. In this work, we propose BiCLUM, a Bilateral Contrastive Learning approach for Unpaired single-cell Multi-omics integration, which simultaneously enforces cell-level and feature-level alignment across modalities. BiCLUM first transforms one modality, such as scATAC-seq, into the data space of another modality, such as scRNA-seq, using prior genomic knowledge. It then learns cell and gene embeddings simultaneously through a bilateral contrastive learning framework, incorporating both cell-level and feature-level contrastive losses. Across multiple RNA+ATAC and RNA+protein datasets, BiCLUM consistently outperforms or matches existing integration methods in both visualization and quantitative benchmarks. Importantly, BiCLUM embeddings preserve biologically meaningful regulatory relationships between chromatin accessibility and gene expression, as evidenced by significantly higher gene-peak correlations than random controls. Downstream analyses further demonstrate that BiCLUM-derived embeddings facilitate transcription factor activity inference, identification of cell-type-specific marker genes, functional enrichment, and cell-cell interaction mapping. Comprehensive hyperparameter sensitivity and ablation analyses further establish BiCLUM as a robust and interpretable framework that not only achieves effective cross-modal alignment but also retains the underlying regulatory and functional landscape across single-cell modalities.

Cell migration in vivo is often guided by chemical signaling, i.e., chemotaxis. For immune cells performing chemotaxis in the organism, this process is influenced by the complex geometry of the tissue environment. In this study, we use a theoretical model of branched cell migration on a network to explore the cellular response to chemical gradients. The model predicts the response of a branched cell to a chemical gradient: how the cell reorients its internal polarity and how it navigates through a complex environment up a chemical gradient. We then compare the model's predictions with experimental observations of neutrophils migrating to the site of a laser-inflicted wound in a zebrafish larva fin, and neutrophils migrating in vitro inside a regular lattice of pillars. We find that the model captures the details of the subcellular response to the chemokine gradient, as well as qualitative characteristics of the large-scale migration, suggesting that the neutrophils behave as fast cells, which explains the functionality of these immune cells.

When receiving a reward after a sequence of multiple events, how do we determine which event caused the reward? This problem, known as temporal credit assignment, can be difficult for humans to solve given the temporal uncertainty in the environment. Research to date has attempted to isolate dimensions of delay and reward during decision-making, but algorithmic solutions to temporal learning problems and the effect of uncertainty on learning remain underexplored. To further our understanding, we adapted a reward learning task that creates a temporal credit assignment problem by combining sequentially delayed rewards, intervening events, and varying uncertainty via the amount of information presented during feedback. Using computational modeling, two learning strategies were developed: an eligibility trace, whereby previously selected actions are updated as a function of the temporal sequence, and a tabular update, whereby only systematically related past actions (rather than unrelated intervening events) are updated. We hypothesized that reduced information uncertainty would correlate with increased use of the tabular strategy, given the model's capacity to incorporate additional feedback information. Both models effectively learned the task, and predicted choices made by participants (N = 142) as well as specific behavioral signatures of credit assignment. Consistent with our hypothesis, the tabular model outperformed the eligibility model under low information uncertainty, as evidenced by more accurate predictions of participants' behavior and an increase in tabular weight. These findings provide new insights into the mechanisms implemented by humans to solve temporal credit assignment and adapt their strategy in varying environments.

Biomedical systems span multiple spatial scales, encompassing tiny functional units to entire organs. Interpreting these systems through image segmentation requires the effective propagation and integration of information across different scales. However, most existing segmentation methods are optimised for single-scale imaging modalities, limiting their ability to capture and analyse small functional units throughout complete human organs. To facilitate multiscale biomedical image segmentation, we utilised Hierarchical Phase-Contrast Tomography (HiP-CT), an advanced imaging modality that can generate 3D multiscale datasets from high-resolution volumes of interest (VOIs) at ca. 1 [Formula: see text]/voxel to whole-organ scans at ca. 20 [Formula: see text]/voxel. Building on these hierarchical multiscale datasets, we developed a deep learning-based segmentation pipeline that is initially trained on manually annotated high-resolution HiP-CT data and then extended to lower-resolution whole-organ scans using pseudo-labels generated from high-resolution predictions and multiscale image registration. As a case study, we focused on glomeruli in human kidneys, benchmarking four 3D deep learning models for biomedical image segmentation on a manually annotated high-resolution dataset extracted from VOIs, at 2.58 to ca. 5 [Formula: see text]/voxel, of four human kidneys. Among them, nnUNet demonstrated the best performance, achieving an average test Dice score of 0.906, and was subsequently used as the baseline model for multiscale segmentation in the pipeline. Applying this pipeline to two low-resolution full-organ data at ca. 25 [Formula: see text]/voxel, the model identified 1,019,890 and 231,179 glomeruli in a 62-year-old donor without kidney diseases and a 94-year-old hypertensive donor, enabling comprehensive morphological analyses, including cortical spatial statistics and glomerular distributions, which aligned well with previous anatomical studies. Our results highlight the effectiveness of the proposed pipeline for segmenting small functional units in multiscale bioimaging datasets and suggest its broader applicability to other organ systems.

Advances in sequencing technologies have enhanced the understanding of gene regulation in cells. In particular, Perturb-seq has enabled high-resolution profiling of the transcriptomic response to genetic perturbations at the single-cell level. This understanding has implications in functional genomics and potentially for identifying therapeutic targets. Various computational models have been developed to predict perturbational effects. While deep learning models excel at interpolating observed perturbational data, they tend to overfit in the lack of enough data and may not generalize well to unseen perturbations. In contrast, mechanistic models, such as linear causal models based on gene regulatory networks, hold greater potential for extrapolation, as they encapsulate regulatory information that can predict responses to unseen perturbations. However, their application has been limited to small studies due to overly simplistic assumptions, making them less effective in handling noisy, large-scale single-cell data. We propose a hybrid approach that combines a mechanistic causal model with variational deep learning, termed Single Cell Causal Variational Autoencoder (SCCVAE). The mechanistic model employs a learned regulatory network to represent perturbational changes as shift interventions that propagate through the learned network. SCCVAE integrates this mechanistic causal model into a variational autoencoder, generating rich, comprehensive transcriptomic responses. Our results indicate that SCCVAE exhibits superior performance over current state-of-the-art baselines for extrapolating to predict unseen perturbational responses. Additionally, for the observed perturbations, the latent space learned by SCCVAE allows for the identification of functional perturbation modules and simulation of single-gene knockdown experiments of varying penetrance, presenting a robust tool for interpreting and interpolating perturbational responses at the single-cell level.