Biology subjects

Osella, M.

Publications and source records attributed to Osella, M..

14 recordsLinked to original sources

bioRxiv2025

Intrinsic dimensionality of single-cell transcriptomic data reveals potency landscapes during cell reprogramming

Cell potency, the ability of a cell to generate other cell types, is a fundamental property that drives development, regeneration, and reprogramming. Recent computational advances have enabled estimation of potency directly from single-cell RNA sequencing (scRNA-seq) data, with intrinsic dimensionality (ID) emerging as a promising, data-driven measure of transcriptional complexity linked to developmental potential. While ID offers an unbiased and interpretable framework, existing implementations have two main limitations: they have been applied in only a narrow range of biological contexts, and rely on clustering, which restricts resolution at the single-cell level. Here, we introduce IDEAS (Intrinsic Dimensionality Estimation Analysis of single-cell RNA sequencing data), a Python-based toolkit that computes both global and single-cell ID from scRNA-seq data, enabling potency scoring without requiring predefined clusters. IDEAS extends ID-based potency estimation to single cells, showing its usefulness in a new biological setting. It offers a clear and reliable way to study cell plasticity, simplifies ID score calculation, and makes the method easier to use across different biological systems, speeding up research on this approach to measuring cell potency. We apply IDEAS to multiple datasets of cellular reprogramming, a dynamic and heterogeneous process in which cells transition between identities. Our analysis reveals that ID scores effectively capture changes in potency, identify partially reprogrammed intermediates, and delineate alternative reprogramming trajectories.

systems biology↗

bioRxiv2024

Exploring the latent space of transcriptomic data withtopic modeling

The availability of high-dimensional transcriptomic datasets is increasing at a tremendous pace, together with the need for suitable computational tools. Clustering and dimensionality reduction methods are popular go-to methods to identify basic structures in these datasets. At the same time, different topic modeling techniques have been developed to organize the deluge of available data of natural language using their latent topical structure. This paper leverages the statistical analogies between text and transcriptomic datasets to compare different topic modeling methods when applied to gene expression data. Specifically, we test their accuracy in the specific task of discovering and reconstructing the tissue structure of the human transcriptome and distinguishing healthy from cancerous tissues. We examine the properties of the latent space recovered by different methods, highlight their differences, and the pros and cons of the methods across different tasks. Finally, we show that the latent topic space can be a useful embedding space, where a basic neural network classifier can annotate transcriptomic profiles with high accuracy.

bioinformatics↗

bioRxiv2024

Topic Modeling analysis of the Allen Human Brain Atlas

The human brain is a complex interconnected structure controlling all elementary and high-level cognitive tasks. It is composed of many regions that exhibit specific distributions of cell types and distinct patterns of functional connections. This complexity is rooted in differential transcription. The constituent cell types of different brain regions express distinctive combinations of genes as they develop and mature, ultimately shaping their functional state in adulthood. How precisely the genetic information of anatomical structures is connected to their underlying biological functions remains an open question in modern neuroscience. A major challenge is the identification of "universal patterns", which do not depend on the particular individual, but are instead basic structural properties shared by all brains. Despite the vast amount of gene expression data available at both the bulk and single-cell levels, this task remains challenging, mainly due to the lack of suitable data mining tools. In this paper, we propose an approach to address this issue based on a hierarchical version of Stochastic Block Modeling. Thanks to its specific choice of priors, the method is particularly effective in identifying these universal features. We use as a laboratory to test our algorithm a dataset obtained from six independent human brains from the Allen Human Brain Atlas. We show that the proposed method is indeed able to identify universal patterns much better than more traditional algorithms such as Latent Dirichlet Allocation or Weighted Correlation Network Analysis. The probabilistic association between genes and samples that we find well represents the known anatomical and functional brain organization. Moreover, leveraging the peculiar "fuzzy" structure of the gene sets obtained with our method, we identify examples of transcriptional and post-transcriptional pathways associated with specific brain regions, highlighting the potential of our approach.

biophysics↗

bioRxiv2024

The intrinsic dimension of gene expression during cell differentiation

Waddingtons epigenetic landscape has long served as a conceptual framework for understanding cell fate decisions. The landscapes geometry encodes the molecular mechanisms that guide the gene expression profiles of uncommitted cells toward terminally differentiated cell types. In this study, we demonstrate that applying the concept of intrinsic dimension to single-cell transcriptomic data can effectively capture trends in expression trajectories, supporting this framework. This approach allows us to define a robust cell potency score without relying on prior biological information. By analyzing an extensive collection of datasets from various species, experimental protocols, and differentiation processes, we validate our method and successfully reproduce established hierarchies of cell type potency.

systems biology↗

bioRxiv2023

Out-of-equilibrium gene expression fluctuations in presence of extrinsic noise

Cell-to-cell variability in protein concentrations is strongly affected by extrinsic noise, especially for highly expressed genes. Extrinsic noise can be due to fluctuations of several possible cellular factors connected to cell physiology and to the level of key enzymes in the expression process. However, how to identify the predominant sources of extrinsic noise in a biological system is still an open question. This work considers a general stochastic model of gene expression with extrinsic noise represented as colored fluctuations of the different model rates, and focuses on the out-of-equilibrium expression dynamics. Combining analytical calculations with stochastic simulations, we fully characterize how extrinsic noise shapes the protein variability during gene activation or inactivation, depending on the prevailing source of extrinsic variability, on its intensity and timescale. In particular, we show that qualitatively different noise profiles can be identified depending on which are the fluctuating parameters. This indicates an experimentally accessible way to pinpoint the dominant sources of extrinsic noise using time-coarse experiments. Author summaryGenetically identical cells living in the same environment may differ in their phenotypic traits. These differences originate from the inherent stochasticity in all cellular processes, starting from the basic process of gene expression. At this level, large part of the variability comes from cell-to-cell differences in the rates of the molecular reactions due to stochasticity in the level of key enzymes or in physiological parameters such as cell volume or growth rate. Which expression rates are predominantly affected by these so-called "extrinsic" fluctuations and how they impact the level of protein concentration are still open research questions. In this work, we tackle the protein fluctuation dynamics while approaching a steady state after gene activation or repression in presence of extrinsic noise. Our analytical results and simulations show the different consequences of alternative dominant sources of extrinsic noise, thus providing an experimentally-accessible way to distinguish them in specific systems.

systems biology↗

bioRxiv2021

Multi-omics Topic Modeling for Breast Cancer Classification

The integration of transcriptional data with other layers of information, such as the post-transcriptional regulation mediated by microRNAs, can be crucial to identify the driver genes and the subtypes of complex and heterogeneous diseases such as cancer. This paper presents an approach based on topic modeling to accomplish this integration task. More specifically, we show how an algorithm based on a hierarchical version of stochastic block modeling can be naturally extended to integrate any combination of omics data. We test this approach on breast cancer samples from the TCGA database, integrating data on messenger RNA, microRNAs and copy number variations. We show that the inclusion of the microRNA layer significantly improves the accuracy of subtype classification. Moreover, some of the hidden structures or "topics" that the algorithm extracts actually correspond to genes and microRNAs involved in breast cancer development, and are associated to the survival probability. Simple SummaryTopic models are algorithms introduced for discovering hidden topics or latent variables in large unstructured text corpora. Leveraging on analogies between texts and gene expression profiles, these algorithms can be used to find structures in expression data. This work presents an application of topic modeling techniques to the identification of breast cancer subtypes. In particular, we extended a specific class of topic models to allow a multi-omics approach. As an illustrative example, considering both messenger RNA and microRNA expression levels, we were able to clearly distinguish healthy from tumor samples, as well as the different breast cancer subtypes. The integration of different layers of information is crucial for the observed classification accuracy. Our approach naturally provides the genes and the microRNAs associated to the specific topics that are used for the sample organization. We show that indeed these topics often contain genes involved in breast cancer development, and are associated to different survival probabilities.

cancer biology↗

bioRxiv2021

The impact of whole genome duplications on the human gene regulatory networks

This work studies the effects of the two rounds of Whole Genome Duplication (WGD) at the origin of the vertebrate lineage on the architecture of the human gene regulatory networks. We integrate information on transcriptional regulation, miRNA regulation, and protein-protein interactions to comparatively analyse the role of WGD and Small Scale Duplications (SSD) in the structural properties of the resulting multilayer network. We show that complex network motifs, such as combinations of feed-forward loops and bifan arrays, deriving from WGD events are specifically enriched in the network. Pairs of WGD-derived proteins display a strong tendency to interact both with each other and with common partners and WGD-derived transcription factors play a prominent role in the retention of a strong regulatory redundancy. Combinatorial regulation and synergy between different regulatory layers are in general enhanced by duplication events, but the two types of duplications contribute in different ways. Overall, our findings suggest that the two WGD events played a substantial role in increasing the multi-layer complexity of the vertebrate regulatory network by enhancing its combinatorial organization, with potential consequences on its overall robustness and ability to perform high-level functions like signal integration and noise control.

systems biology↗

bioRxiv2021

Emergent Statistical Laws in Single-Cell Transcriptomic Data

Large scale data on single-cell gene expression have the potential to unravel the specific transcriptional programs of different cell types. The structure of these expression datasets suggests a similarity with several other complex systems that can be analogously described through the statistics of their basic building blocks. Transcriptomes of single cells are collections of messenger RNA abundances transcribed from a common set of genes just as books are different collections of words from a shared vocabulary, genomes of different species are specific compositions of genes belonging to evolutionary families, and ecological niches can be described by their species abundances. Following this analogy, we identify several emergent statistical laws in single-cell transcriptomic data closely similar to regularities found in linguistics, ecology or genomics. A simple mathematical framework can be used to analyze the relations between different laws and the possible mechanisms behind their ubiquity. Importantly, treatable statistical models can be useful tools in transcriptomics to disentangle the actual biological variability from general statistical effects present in most component systems and from the consequences of the sampling process inherent to the experimental technique. Author summaryGene expression profiles represent how different cells use their genetic information. Similarly, books are specific collections of words chosen from a shared vocabulary, and many complex systems can be ultimately described by the statistics of their basic components. Leveraging on this analogy, we identified several emergent statistical laws in single-cell transcriptomic data that are universally found in complex component systems. A simple mathematical description sets these laws in a treatable quantitative framework and represents a useful tool for dissecting the different sources of gene expression variability.

systems biology↗

bioRxiv2021

The anatomical logic of smell

The sense of smell helps us navigate the environment, but its molecular architecture and underlying logic remain unknown. The spatial location of odorant receptor genes (Olfrs) in the nose is widely thought to be independent of the structural diversity of the odorants they detect. Using spatial transcriptomics, we created a genome-wide 3D atlas of the mouse olfactory mucosa (OM). Topographic maps of genes differentially expressed in space reveal that both Olfrs and non-Olfrs are distributed in a continuous and overlapping fashion over five broad zones in the OM. The spatial locations of Olfrs correlate with the mucus solubility of the odorants they recognize, providing direct evidence for the chromatographic theory of olfaction. This resource resolved the molecular architecture of the mouse OM, and will inform future studies on mechanisms underlying Olfr gene choice, axonal pathfinding, patterning of the nervous system, and basic logic for the peripheral representation of smell.

neuroscience↗

bioRxiv2021

Role of protein degradation in growth laws

Growing cells adopt common basic strategies to achieve optimal resource allocation under limited resource availability. Our current understanding of such "growth laws" neglects degradation, assuming that it occurs slowly compared to the cell cycle duration. Here we argue that this assumption cannot hold at slow growth, leading to important consequences. We propose a simple framework showing that at slow growth protein degradation is balanced by a fraction of "maintenance" ribosomes. Consequently, active ribosomes do not drop to zero at vanishing growth, but as growth rate diminishes, an increasing fraction of active ribosomes performs maintenance. Through a detailed analysis of compiled data, we show that the predictions of this model agree with data from E. coli and S. cerevisiae. Intriguingly, we also find that protein degradation increases at slow growth, which we interpret as a consequence of active waste management and/or recycling. Our results highlight protein turnover as an underrated factor for our understanding of growth laws across kingdoms. Author summaryThe idea that simple quantitative relationships relate cell physiology to cellular composition dates back to the 1950s, but the recent years saw a leap in our understanding of such "growth laws", with relevant implications regarding the interdependence between growth, metabolism and biochemical networks. However, recent works on nutrient-limited growth mainly focused on laboratory conditions that are favourable to growth. Thus, our current mathematical understanding of the growth laws neglects protein degradation, under the argument that it occurs slowly compared to the timescale of the cell cycle. Instead, at slow growth the timescales of mass loss from protein degradation and dilution become comparable. In this work, we propose that protein degradation shapes the quantitative relationships between ribosome allocation and growth rate, and determines a fraction of ribosomes that do not contribute to growth and need to remain active to balance degradation.

systems biology↗

bioRxiv2020

A Topic model analysis of TCGA transcriptomic data

Topic modelling is a widely used technique to extract relevant information from large arrays of data. The problem of finding a topic structure in a dataset was recently recognized to be analogous to the community detection problem in network theory. Leveraging on this analogy, a new class of topic modelling strategies has been introduced to overcome some of the limitations of classical methods. This paper applies these recent ideas to TCGA transcriptomic data on breast and lung cancer. The established cancer subtype organization is well reconstructed in the inferred latent topic structure. Moreover, we identify specific topics that are enriched in genes known to play a role in the corresponding disease and are strongly related to the survival probability of patients. Finally, we show that a simple neural network classifier operating in the low dimensional topic space is able to predict with high accuracy the cancer subtype of a test expression sample.

cancer biology↗

bioRxiv2019

Genomic duplications shaped current retrotransposon position distribution in human

Retrotransposons, DNA sequences capable of creating copies of themselves, compose about half of the human genome and played a central role in the evolution of mammals. Their current position in the host genome is the result of the retrotranscription process and of the following host genome evolution. We apply a model from statistical physics to show that the genomic distribution of the two most populated classes of retrotransposons in human deviates from random placement, and that this deviation increases with time. The time dependence suggests a major role of the host genome dynamics in shaping the current retrotransposon distributions. In fact, these distributions can be explained by a stochastic process of random placement due to retrotranscription followed by genome expansion and sequence duplications. Besides the inherent interest in understanding the origin of current retrotransposon distributions, this work sets a general analytical framework to analyze quantitatively the effects of genome evolutionary dynamics on the distribution of genomic elements.

biophysics↗

bioRxiv2018

Dissecting the control mechanisms for DNA replication and cell division in E. coli

Understanding how single E. coli cells coordinate the timing of cell division with genome replication would unlock a classic problem of biology, and open the way to address cell-cycle progression at the single-cell level. Several recent studies produced new data and proposed different models, based on the hypothesis that replication-segregation is the bottleneck process for cell division. However, due to the apparent contrast in both experimental results and proposed mechanisms, the emerging picture is fragmented and unclear. In this work, we re-evaluate jointly available data and models, and we show that, while each model contains useful insights, none of the proposed models, as well as generalizations based on the same assumptions, correctly describes all the correlation patterns observed in data. This analysis leads us to conclude that the assumption that replication is the bottleneck process for cell division is too restrictive. Instead, we propose that two concurrent cycles responsible for division and initiation of DNA replication together set the time of cell division. This framework correctly captures available data and allows us to select a nearly constant added size per origin between subsequent initiations as the most likely mechanism setting initiation of replication.

cell biology↗

bioRxiv2018

Concurrent processes set E. coli cell division

A cell can divide only upon completion of chromosome segregation, or its daughters would lose genetic material [1, 2]. In E. coli bacteria, the prevalent view is that cells divide a fixed amount of time after they start to copy the chromosomes [3, 4], and a known pathway prevents cells from dividing if the chromosomes interfere with the cytokinesis machinery [5]. However, whether completion of segregation is typically the bottleneck process for the decision to divide has never been stringently tested on single cells. We show how key trends in single-cell data lead to challenge the classic idea of replication-segregation limiting cell division. Instead, the data agree with a model where two concurrent processes (setting replication initiation and inter-division time) set cell division on competing time scales. During each cell cycle, division is set by the slowest process (an \" ...

cell biology↗