Biology subjects

Martini, L.

Publications and source records attributed to Martini, L..

12 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

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

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↗

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

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↗

bioRxiv2022

Detecting and dissecting signaling crosstalk via the multilayer network integration of signaling and regulatory interactions

The versatility of cellular response arises from the communication, or crosstalk, of signaling pathways in a complex network of signaling and transcriptional regulatory interactions. Understanding the various mechanisms underlying crosstalk on a global scale requires untargeted computational approaches. We present a network-based statistical approach, MuXTalk, that uses high-dimensional edges called multilinks to model the unique ways in which signaling and regulatory interactions can interface. We demonstrate that the signaling-regulatory interface is located primarily in the intermediary region between signaling pathways where crosstalk occurs, and that multilinks can differentiate between distinct signaling-transcriptional mechanisms. Using statistically over-represented multilinks as proxies of crosstalk, we predict crosstalk among 60 signaling pathways, expanding currently available crosstalk databases by more than five-fold. MuXTalk surpasses existing methods in terms of prediction performance, identifies additions to manual curation efforts, and pinpoints potential mediators of crosstalk for each prediction. Moreover, it accommodates the inherent context-dependence of crosstalk, allowing future applications to cell type- and disease-specific crosstalk.

systems biology↗

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↗

bioRxiv2021

Meta-Analysis of cortical inhibitory interneurons markers landscape and their performances in scRNA-seq studies.

The mammalian cortex contains a great variety of neuronal cells. In particular, GABAergic interneurons, which play a major role in neuronal circuit function, exhibit an extraordinary diversity of cell types. In this regard, single-cell RNA-seq analysis is crucial to study cellular heterogeneity. To identify and analyze rare cell types, it is necessary to reliably label cells through known markers. In this way, all the related studies are dependent on the quality of the employed marker genes. Therefore, in this work, we investigate how a set of chosen inhibitory interneurons markers perform. The gene set consists of both immunohistochemistry-derived genes and single-cell RNA-seq taxonomy ones. We employed various human and mouse datasets of the brain cortex, consequently processed with the Monocle3 pipeline. We defined metrics based on the relations between unsupervised cluster results and the marker expression. Specifically, we calculated the specificity, the fraction of cells expressing, and some metrics derived from decision tree analysis like entropy gain and impurity reduction. The results highlighted the strong reliability of some markers but also the low quality of others. More interestingly, though, a correlation emerges between the general performances of the genes set and the experimental quality of the datasets. Therefore, the proposed method allows evaluating the quality of a dataset in relation to its reliability regarding the inhibitory interneurons cellular heterogeneity study.

bioinformatics↗

bioRxiv2021

Assessment of community efforts to advance computational prediction of protein-protein interactions

Comprehensive insights from the human protein-protein interaction (PPI) network, known as the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of new PPIs. Many such approaches have been proposed. However, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 24 representative network-based methods to predict PPIs across five different interactomes, including a synthetic interactome generated by the duplication-mutation-complementation model, and the interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. We selected the top-seven methods through a computational validation on the human interactome. We next experimentally validated their top-500 predicted PPIs (in total 3,276 predicted PPIs) using the yeast two-hybrid assay, finding 1,177 new human PPIs (involving 633 proteins). Our results indicate that task-tailored similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods. Through experimental validation, we confirmed that the top-ranking methods show promising performance externally. For example, from the top 500 PPIs predicted by an advanced similarity-base method [MPS(B&T)], 430 were successfully tested by Y2H with 376 testing positive, yielding a precision of 87.4%. These results establish advanced similarity-based methods as powerful tools for the prediction of human PPIs.

systems biology↗

bioRxiv2020

Dynamics of chromosomal target search by a membrane-integrated one-component receptor

Membrane proteins account for about one third of the cellular proteome, but it is still unclear how dynamic they are and how they establish functional contacts with cytoplasmic interaction partners. Here, we consider a membrane-integrated one-component receptor that also acts as a transcriptional activator, and analyze how it kinetically locates its specific binding site on the genome. We focus on the case of CadC, the pH receptor of the acid stress response Cad system in E. coli. CadC is a prime example of a one-component signaling protein that directly binds to its cognate target site on the chromosome to regulate transcription. We combined fluorescence microscopy experiments, mathematical analysis, and kinetic Monte Carlo simulations to probe this target search process. Using fluorescently labeled CadC, we measured the time from activation of the receptor until successful binding to the DNA in single cells, exploiting that stable receptor-DNA complexes are visible as fluorescent spots. Our experimental data indicate that CadC is highly mobile in the membrane and finds its target by a 2D diffusion and capture mechanism. DNA mobility is constrained due to the overall chromosome organization, but a labeled DNA locus in the vicinity of the target site appears sufficiently mobile to randomly come close to the membrane. Relocation of the DNA target site to a distant position on the chromosome had almost no effect on the mean search time, which was between four and five minutes in either case. However, a mutant strain with two binding sites displayed a mean search time that was reduced by about a factor of two. This behavior is consistent with simulations of a coarse-grained lattice model for the coupled dynamics of DNA within a cell volume and proteins on its surface. The model also rationalizes the experimentally determined distribution of search times. Overall our findings reveal that DNA target search does not present a much bigger kinetic challenge for membrane-integrated proteins than for cytoplasmic proteins. More generally, diffusion and capture mechanisms may be sufficient for bacterial membrane proteins to establish functional contacts with cytoplasmic targets. Author summaryAdaptation to changing environments is vital to bacteria and is enabled by sophisticated signal transduction systems. While signal transduction by two-component systems is well studied, the signal transduction of membrane-integrated one-component systems, where one protein performs both sensing and response regulation, are insufficiently understood. How can a membrane-integrated protein bind to specific sites on the genome to regulate transcription? Here, we study the kinetics of this process, which involves both protein diffusion within the membrane and conformational fluctuations of the genomic DNA. A well-suited model system for this question is CadC, the signaling protein of the E. coli Cad system involved in pH stress response. Fluorescently labeled CadC forms visible spots in single cells upon stable DNA-binding, marking the end of the protein-DNA search process. Moreover, the start of the search is triggered by a medium shift exposing cells to pH stress. We probe the underlying mechanism by varying the number and position of DNA target sites. We combine these experiments with mathematical analysis and kinetic Monte Carlo simulations of lattice models for the search process. Our results suggest that CadC diffusion in the membrane is pivotal for this search, while the DNA target site is just mobile enough to reach the membrane.

biophysics↗