Biology subjects

Di Carlo, S.

Publications and source records attributed to Di Carlo, S..

At least 19 recordsLinked to original sources

bioRxiv2026

ATLAS: a scverse-compatible package for multi-omic single-cell trajectory inference integration

Single-cell trajectory inference is widely used to study cellular differentiation and fate decisions, yet most methods rely solely on transcriptomic data and therefore capture only part of the regulatory processes underlying cell-state transitions. Here we present ATLAS (Advanced Trajectory Learning from multi-omics At Single-cell resolution), a scverse-compatible Python package for trajectory inference from paired single-cell RNA-seq and ATAC-seq data. ATLAS integrates transcriptomic and chromatin accessibility information through Weighted Nearest Neighbor graphs, enabling both modalities to jointly inform pseudotime estimation, terminal-state identification, and fate probability inference within a unified multi-omic representation. Across synthetic and real datasets, ATLAS reconstructs coherent developmental trajectories, captures progressive fate commitment, and resolves biologically meaningful lineage structures, highlighting the value of multi-omic integration for characterizing cellular developmental dynamics. In addition, ATLAS enables joint analysis of transcription factor expression and accessibility-derived target-gene activity along pseudotime, providing insights into regulatory programs spanning transcriptomic and epigenomic layers that are not readily detectable from unimodal data. As a proof of concept, ATLAS recapitulates known hair follicle regulatory programs and reveals coherent multi-omic trajectories in which Lef1-associated regulatory patterns are linked to hair shaft differentiation. Overall, ATLAS provides an interoperable and biologically informative framework for studying cellular differentiation and regulatory dynamics in single-cell multi-omics experiments.

bioinformatics↗

bioRxiv2026

On the state of protein function prediction: a report on the fourth CAFA challenge

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). ResultsCAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced "partial knowledge" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.

bioinformatics↗

bioRxiv2026

Integrative Comparison of GeneHancer and Single-Cell Co-accessibility Reveals Active Enhancer-Gene Interactions

Linking enhancers to their target genes remains challenging due to the context-independent nature of curated annotations and the noise inherent in data-driven predictions. GeneHancer provides a comprehensive catalogue of enhancer-gene associations, but many elements are inactive in specific biological settings. Conversely, co-accessibility inferred from single-cell chromatin accessibility data captures sample-specific regulatory structure but may reflect indirect or non-functional interactions. This work integrates these complementary perspectives by comparing GeneHancer annotations with co-accessibility networks derived from a human PBMC Multiome dataset. Using Circe to infer peak-peak co-accessibility and GRAIGH to map peaks onto GeneHancer elements, this approach identifies enhancer-gene associations supported both by prior evidence and by accessibility patterns in the dataset. Only a small subset of GeneHancer links is validated by co-accessibility, yet these conserved associations display substantially higher cell-type specificity and stronger accessibility-expression concordance than either the full or "Elite" GeneHancer sets. This refined subset isolates regulatory interactions that are both biologically plausible and active in the sample, reducing redundancy and improving interpretability. Our results show that integrating curated enhancer annotations with single-cell epigenomic evidence yields a focused, high-confidence regulatory map suited for analyzing transcriptional regulation and cell identity in a dataset-specific manner.

bioinformatics↗

bioRxiv2025

Spike Train Scalograms (STS): a Deep Learning Classification Pipeline for Neuronal Cell Types

Classifying neuronal cell types is crucial for understanding the intricate circuitry of the cerebral cortex, which comprises diverse specialized neurons essential for brain function. Traditional Machine Learning (ML) approaches rely on manually engineered electrophysiological (EP) features, often overlooking subtle and complex patterns within spike train data. This study introduces a novel Spike Train Scalograms (STS)-based Deep Learning (DL) pipeline that integrates Continuous Wavelet Transform (CWT) scalograms with pre-trained Convolutional Neural Network (CNN) architectures to classify neuronal cell types with high accuracy. Utilizing patch-clamp EP recordings from 5,590 murine cortical neurons, the pipeline transforms spike trains into time-frequency representations via CWT, capturing both transient and sustained signal characteristics. These scalograms are then processed by fine-tuned CNN architectures, including InceptionV3, which achieved a balanced accuracy and weighted F1-Score of 90.53% and 90.03%, respectively. The STS pipeline effectively distinguishes between major neuronal types such as Pvalb, Sst, Vip/Lamp5, and Excitatory neurons, even in the presence of class imbalances. Moreover, an explainability analysis using saliency maps and SHAP revealed high correspondence between the DL approach, the ML baseline and biological knowledge of these neuronal types. The results demonstrate that, by combining an advanced spectral analysis with DL techniques, neurons can be classified with high accuracy, employing only two raw sweeps rather then the full stimulation range required by shallow approaches.

bioinformatics↗

bioRxiv2024

Start&Stop - a PhysiCell and PhysiBoSS 2.0 add-on for interactive simulation control

In computational biology, in silico simulators are vital for exploring and understanding the behavior of complex biological systems. Hybrid multi-level simulators, such as PhysiCell and PhysiBoSS 2.0, integrate multiple layers of biological complexity, providing deeper insights into emergent patterns. However, one key limitation of these simulators is the inability to adjust simulation parameters once the simulation has started, which hinders the interactive exploration and adaptation of dynamic protocols ranging from biofabrication to in vitro pharmacological testing. To address this challenge, we introduce the Start & Stop add-on for PhysiCell and PhysiBoSS 2.0. This add-on offers multi-level state preservation and multi-modal stop control, triggered by simulation time or cell conditions, enabling users to pause a simulation, adjust parameters, and then resume from the exact halted state. We validate Start & Stop using two well-established PhysiBoSS 2.0 use cases, a tumor spheroid 3T3 mouse fibroblasts use case under tumor necrosis factor (TNF) stimulation, and a lung cancer cell line invasion simulation, demonstrating that it preserves the simulators original behavior while enabling interactive configuration changes that facilitate the exploration of diverse and adaptive treatment strategies. By enhancing flexibility and user interaction, Start & Stop makes PhysiCell and PhysiBoSS 2.0 more akin to real in vitro scenarios, thus expanding the range of potential simulations and advancing more effective protocol development in a variety of applications.

bioinformatics↗

bioRxiv2024

Impaired erythroid maturation in murine embryos upon loss of the preeclampsia-associated serine protease prostasin

In humans, the membrane-bound serine protease prostasin encoded by Prss8 is associated with preeclampsia, a gestational hypertension disorder affecting blood supply of the placenta. Mice deficient in Prss8 resulted in the death of embryos at embryonic day (E) 14.5 and it was characterized by impaired placental labyrinth maturation and vascularization. A pale phenotype was observed in these embryos, suggesting ineffective erythropoiesis. Thus, in this study we analyzed this phenotype further in Prss8-/- embryos at E11.5 and E12.5. We found a reduced number of fetal erythroblasts in placenta, yolk sac and fetal liver of Prss8-/- embryos, while the reticulocyte number was increased, suggesting a defective terminal erythroid differentiation. Further, single-cell RNA sequencing (scRNA-seq) analyses of aorta-gonad-mesonephros (AGM) revealed an upregulation of several ribosomal genes associated with Diamond-Blackfan anemia in erythroid cells of Prss8-/- (KO) embryos. These cells showed a lower capacity to maturate into erythrocytes in vivo and in vitro, despite hematopoietic cells (HSCs) being produced normally. We suggested prostasin influenced erythropoiesis in a cell-extrinsic manner, since Prss8 expression was not detected in erythroid cells but highly expressed in ectoderm-like cells within the AGM. Congruently, while yolk sac-derived cells displayed no erythroid maturation defect in vitro, the yolk sac vascular remodeling in KO embryos was impaired as evidenced by reduced secondary branching likely as a consequence of the reduced blood flow. Our findings unveiled a novel role for this serine protease in terminal maturation of erythrocytes in the fetal liver and open new research avenues for understanding the physiological mechanism of prostasin and its pathological implications. Key PointsO_LIPrss8 deficiency causes transcriptional changes in erythroid progenitor cells in the AGM leading to impaired embryonic erythropoiesis C_LIO_LIOverexpression of Rpl and Rps genes by erythroid cells lacking Prss8 leads to defective erythropoiesis and embryonic lethality C_LI

developmental biology↗

bioRxiv2024

Fast and Accurate LSTM Meta-modeling of TNF-induced Tumor Resistance In Vitro

Multi-level, hybrid models and simulations, among other methods, are essential to enable predictions and hypothesis generation in systems biology research. However, the computational complexity of these models poses a bottleneck, limiting the applicability of methodologies relying on large number of simulations, such as the Optimization via Simulation (OvS) of complex biological processes. Meta-models based on approximate surrogate models simplify multi-level simulations, maintaining accuracy while reducing computational costs. Among Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM) networks are well suited to handle sequential data, which often characterizes biological simulations. This paper presents an LSTM-based surrogate modeling approach for multi-level simulations of complex biological processes. Validation relies on the simulation of Tumor Necrosis Factor (TNF) administration to a 3T3 mouse fibroblasts tumor spheroid based on PhysiBoSS 2.0, a hybrid agent-based multi-level modeling framework. Results show that the proposed LSTM meta-model is accurate and fast compared with the simulator. In fact, it infers simulated behavior with an average relative error of 7.5%. Moreover, it is at least five orders of magnitude faster. Even considering the cost of training, this approach provides a faster, more accurate, and reusable surrogate of multi-scale simulations in computationally complex tasks, such as model-based OvS of biological processes.

bioinformatics↗

bioRxiv2024

A starting guide on multi-omic single-cell data joint analysis: basic practices and results

Multi-omics single-cell data represent an excellent opportunity to investigate biological complexity in general and generate new insights into the biological complexity of heterogeneous multicellular populations. Considering one omics pool at a time captures partial cellular states, while combining data from different omics collections allows for a better reconstruction of the intricacies of cell regulations at a particular time. However, multi-omics data provide only an opportunity. Computational approaches can leverage such opportunities, given that they raise the challenge of consistent data integration and multi-omics analysis. This work showcases a bioinformatic workflow combining existing methods and packages to analyze transcriptomic and epigenomic single-cell data separately and jointly, generating a new, more complete understanding of cellular heterogeneity.

bioinformatics↗

bioRxiv2024

Cross-omic Transcription Factors meta-analysis: an insight on TFs accessibility and expression correlation

It is well-known how sequencing technologies propelled cellular biology research in recent years, giving an incredible insight into the basic mechanisms of cells. Single-cell RNA sequencing is at the front in this field, with Single-cell ATAC sequencing supporting it and becoming more popular. In this regard, multi-modal technologies play a crucial role, allowing the possibility to perform the mentioned sequencing modalities simultaneously on the same cells. Yet, there still needs to be a clear and dedicated way to analyze this multi-modal data. One of the current methods is to calculate the Gene Activity Matrix (GAM), which summarizes the accessibility of the genes at the genomic level, to have a more direct link with the transcriptomic data. However, this concept is not well-defined, and it is unclear how various accessible regions impact the expression of the genes. Moreover, the transcription process is highly regulated by the Transcription Factors that binds to the different DNA regions. Therefore, this work presents a continuation of the meta-analysis of Genomic-Annotated Gene Activity Matrix (GAGAM) contributions, aiming to investigate the correlation between the TFs expression and motif information in the different functional genomic regions to understand the different Transcription Factors (TFs) dynamics involved in different cell types.

bioinformatics↗

bioRxiv2024

Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems

BackgroundThe Biology System Description Language (BiSDL) is an accessible, easy-to-use computational language for multicellular synthetic biology. It allows synthetic biologists to represent spatiality and multi-level cellular dynamics inherent to multicellular designs, filling a gap in the state of the art. Developed for designing and simulating spatial, multicellular synthetic biological systems, BiSDL integrates high-level conceptual design with detailed low-level modeling, fostering collaboration in the Design-Build-Test-Learn cycle. BiSDL descriptions directly compile into Nets-Within-Nets (NWNs) models, offering a unique approach to spatial and hierarchical modeling in biological systems. ResultsBiSDLs effectiveness is showcased through three case studies on complex multicellular systems: a bacterial consortium, a synthetic morphogen system and a conjugative plasmid transfer process. These studies highlight the BiSDL proficiency in representing spatial interactions and multi-level cellular dynamics. The language facilitates the compilation of conceptual designs into detailed, simulatable models, leveraging the NWNs formalism. This enables intuitive modeling of complex biological systems, making advanced computational tools more accessible to a broader range of researchers. ConclusionsBiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for designing and simulating complex biological systems with an emphasis on spatiality and cellular dynamics. Its introduction has the potential to transform research and development in synthetic biology, allowing for deeper insights and novel applications in understanding and manipulating multicellular systems.

synthetic biology↗

bioRxiv2023

Optimization of synthetic oscillatory biological networks through Reinforcement Learning

In the expanding realm of computational biology, Reinforcement Learning (RL) emerges as a novel and promising approach, especially for designing and optimizing complex synthetic biological circuits. This study explores the application of RL in controlling Hopf bifurcations within ODE-based systems, particularly under the influence of molecular noise. Through two case studies, we demonstrate RLs capabilities in navigating biological systems inherent non-linearity and high dimensionality. Our findings reveal that RL effectively identifies the onset of Hopf bifurcations and preserves biological plausibility within the optimized networks. However, challenges were encountered in achieving persistent oscillations and matching traditional algorithms computational speed. Despite these limitations, the study highlights RLs significant potential as an instrumental tool in computational biology, offering a novel perspective for exploring and optimizing oscillatory dynamics within complex biological systems. Our research establishes RL as a promising strategy for manipulating and designing intricate behaviors in biological networks.

bioengineering↗

bioRxiv2023

GRAIGH: Gene Regulation accessibility integrating GeneHancer database

Single-cell assays for transposase-accessible chromatin sequencing data are one of the most powerful tools for studying the epigenetic heterogeneity of cell populations. However, the chromatin accessibility landscape is not well understood and lacks a proper way to interpret it. This work proposes Gene Regulation Accessibility Integrating GeneHancer (GRAIGH), a novel approach to the interpretation of genome accessibility through the integration of the GeneHancer database information, which describes genome-wide enhancer-to-gene associations. Firstly, this paper presents the methods for integrating GeneHancer with scATAC-seq data, creating a new matrix where the features are the GeneHancer elements IDs instead of the accessibility peaks. Secondly, it investigates its capability to analyze the data and detect cellular heterogeneity. In particular, this work shows that the GeneHancer elements are selectively accessible for distinct cell types, and more importantly, their connected genes are precisely known marker genes. Moreover, it investigates the specificity of GeneHancer elements accessibility, demonstrating their high selectivity against the gene activity.

bioinformatics↗

bioRxiv2023

Neuronal Spike Shapes (NSS): a simple approach to study electrophysiological data for heterogeneity investigation.

The mammalian brain exhibits a remarkable diversity of neurons, contributing to its intricate architecture and functional complexity. The analysis of multimodal single-cell datasets enables the investigation of cell types and states heterogeneity. In this study, we introduce the Neuronal Spike Shapes (NSS), a straightforward approach for the exploration of excitability states of neurons based on their Action Potential (AP) waveforms. The NSS method describes the AP waveform based on a triangular representation complemented by a set of derived electrophysiological (EP) features. To support this hypothesis, we validate the proposed approach on two datasets of murine cortical neurons, focusing it on GABAergic neurons. The validation process involves a combination of NSS-based clustering analysis, features exploration, Differential Expression (DE), and Gene Ontology (GO) enrichment analysis. Results show that the NSS-based analysis captures neuronal excitability states that possess biological relevance independently of cell subtype. In particular, Neuronal Spike Shapes (NSS) captures, among others, a well-characterized fast-spiking excitability state, supported by both electrophysiological and transcriptomic validation. Gene Ontology Enrichment Analysis reveals voltage-gated potassium (K+) channels as specific markers of the identified NSS partitions. This finding strongly corroborates the biological relevance of NSS partitions as excitability states, as the expression of voltage-gated K+ channels regulates the hyperpolarization phase of the AP, being directly implicated in the regulation of neuronal excitability.

bioengineering↗

bioRxiv2023

A deep reinforcement learning approach to the optimization of epithelial sheets biofabrication

Tissue Engineering (TE) and Regenerative Medicine (RM) aim to replicate and replace tissues for curing disease. However, full tissue integration and homeostasis are still far from reach. Biofabrication is an emerging field that identifies the processes required for generating biologically functional products with the desired structural organization and functionality and can potentially revolutionize the regenerative medicine domain, which aims to use patients cells to restore the structure and function of damaged tissues and organs. However, biofabrication still has limitations in the quality of processes and products. Biofabrication processes are often improved empirically, but this is slow, costly, and provides partial results. Computational approaches can tap into biofabrication underused potential, supporting analysis, modeling, design, and optimization of biofabrication processes, speeding up their improvement towards a higher quality of products and subsequent higher clinical relevance. This work proposes a reinforcement learning-based computational design space exploration methodology to generate optimal in-silico protocols for the simulated fabrication of epithelial sheets. The optimization strategy relies on a Deep Reinforcement Learning (DRL) algorithm, the Advantage-Actor Critic, which relies on a neural network model for learning. In contrast, simulations rely on the PalaCell2D simulation framework. Validation demonstrates the proposed approach on two protocol generation targets: maximizing the final number of obtained cells and optimizing the spatial organization of the cell aggregate.

bioinformatics↗

bioRxiv2023

Meta-analysis of gene activity (MAGA) contributions and correlation with gene expression, through GAGAM.

It is well-known how sequencing technologies propelled cellular biology research in the latest years, giving an incredible insight into the basic mechanisms of cells. Single-cell RNA sequencing is at the front in this field, with Single-cell ATAC sequencing supporting it and becoming more popular. In this regard, multi-modal technologies play a crucial role, allowing the possibility to perform the mentioned sequencing modalities simultaneously on the same cells. Yet, there still needs to be a clear and dedicated way to analyze this multi-modal data. One of the current methods is to calculate the Gene Activity Matrix, which summarizes the accessibility of the genes at the genomic level, to have a more direct link with the transcriptomic data. However, this concept is not well-defined, and it is unclear how various accessible regions impact the expression of the genes. Therefore, this work presents a meta-analysis of the Gene Activity matrix based on the Genomic-Annotated Gene Activity Matrix model, aiming to investigate the different influences of its contributions on the activity and their correlation with the expression. This allows having a better grasp on how the different functional regions of the genome affect not only the activity but also the expression of the genes.

bioinformatics↗

bioRxiv2023

Computational modeling and optimization of biofabrication in Tissue Engineering and Regenerative Medicine -- a literature review

This literature review rigorously examines the growing scientific interest in computational methods for Tissue Engineering and Regenerative Medicine biofabrication, a leading-edge area in biomedical innovation, emphasizing the need for accurate, multi-stage, and multi-component biofabrication process models. The paper presents a comprehensive bibliometric and contextual analysis, followed by a literature review, to shed light on the vast potential of computational methods in this domain. It reveals that most existing methods focus on single biofabrication process stages and components, and there is a significant gap in approaches that utilize accurate models encompassing both biological and technological aspects. This analysis underscores the indispensable role of these methods in under-standing and effectively manipulating complex biological systems and the necessity for developing computational methods that span multiple stages and components. The review concludes that such comprehensive computational methods are essential for developing innovative and efficient Tissue Engineering and Regenerative Medicine biofabrication solutions, driving forward advancements in this dynamic and evolving field. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/530995v2_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1239f25org.highwire.dtl.DTLVardef@1a53bd9org.highwire.dtl.DTLVardef@fc4c68org.highwire.dtl.DTLVardef@1fc75b1_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

A methodology for co-simulation-based optimization of biofabrication protocols

Biofabrication processes are complex and often unsatisfactory. Trial-and-error methods are costly and yield only incremental innovation, starting from sub-optimal and poorly represented existing processes. Although computational techniques might support efficient process design to find optimal process configurations, intelligent computational approaches must comprise biological complexity to provide meaningful insights. This paper proposes a novel co-simulation-based optimization methodology for the systematic design of protocols for cell culture and biofabrication. The proposed strategy integrates evolutionary computation and simulation for efficient design space exploration and assessment of candidate protocols. A generic library supports the modular and flexible composition of multiscale and multidomain co-simulation scenarios. The feasibility of the presented approach was demonstrated in the automatic generation of protocols for the biofabrication of an epithelial cell monolayer. The results are twofold. First, the prototype co-simulation library helps build flexible, loosely coupled simulation scenarios. Second, the in-silico experimentation on the use case shows that the proposed approach is a viable first step towards standard and automated design in biofabrication.

bioinformatics↗

bioRxiv2022

GAGAM: a genomic annotation-based enrichment of scATAC-seq data for Gene Activity Matrix

Single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) is rapidly becoming a powerful technology to assess the epigenetic landscape of thousands of cells. However, the current great sparsity of the resulting data poses significant challenges to their interpretability and informativeness. Different computational methods are available, proposing ways to generate significant features from accessibility data and process them to obtain meaningful results. In particular, the most common way to interpret the raw scATAC-seq data is through peak-calling, generating the peaks as features. Nevertheless, this method is dataset-dependent because the peaks are related to the given dataset and can not be directly compared between different experiments. For this reason, this study wants to improve on the concept of the Gene Activity Matrix (GAM), which links the accessibility data to the genes, by proposing a Genomic-Annotated Gene Activity Matrix (GAGAM), which aims to label the peaks and link them to the genes through functional annotation of the whole genome. Using genes as features solves the problem of the feature dataset dependency allowing for the link of gene accessibility and expression. The latter is crucial for gene regulation understanding and fundamental for the increasing impact of multi-omics data. Results confirm that our method performs better than the previous GAMs.

bioinformatics↗