Biology subjects

Piermarocchi, C.

Publications and source records attributed to Piermarocchi, C..

12 recordsLinked to original sources

bioRxiv2026

The LIF-LIFR Axis Promotes Liver Regeneration via Modulation of Angiogenesis and HGF Release from LSECs

Liver sinusoidal endothelial cells (LSECs) play essential roles in liver regeneration after injury, but the underlying mechanisms remain incompletely defined. Here we report that leukemia inhibitory factor (LIF), which is rapidly induced after liver injury, acts as a key regulator of LSECs-driven liver regeneration through interaction with LSECs-enriched LIF receptor (LIFR). LIF directly stimulates LSECs proliferation and induces hepatocyte growth factor (HGF) release in a dose-dependent manner via LIFR signaling in LSECs, thereby indirectly promoting hepatocyte proliferation. Systemic LIF neutralization or endothelial cells (ECs)-specific Lifr loss impairs liver regeneration, whereas low-titer AAV-mediated LIF expression increases vascular density, elevates circulating HGF, and improves early liver recovery after partial hepatectomy (PHx) in mice. Together, these findings establish LIF-LIFR as a previously unrecognized endothelial axis to promote hepatocyte proliferation and suggest potential therapeutic strategies to enhance liver repair in patients. HighlightsO_LILIF is upregulated after liver injury and LIF neutralization impairs liver recovery. C_LIO_LILIFR displays the highest expression in ECs; endothelial-specific Lifr deletion delays liver regeneration after injury. C_LIO_LILIF mediates a positive feedback loop including LSECs proliferation as well as HGF release via LIFR pathway. C_LIO_LILIF overexpression increases liver-to-body weight ratio in a dose-dependent manner and accelerates liver regeneration at early stage. C_LI Abstract Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/707802v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@13b42feorg.highwire.dtl.DTLVardef@1ab6390org.highwire.dtl.DTLVardef@115c157org.highwire.dtl.DTLVardef@1486993_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2023

Time-resolved molecular measurements reveal changes in astronauts during spaceflight

From the early days of spaceflight to current missions, astronauts continue to be exposed to multiple hazards that affect human health, including low gravity, high radiation, isolation during long-duration missions, a closed environment and distance from Earth. Their effects can lead to adverse physiological changes and necessitate countermeasure development and/or longitudinal monitoring. A time-resolved analysis of biological signals can detect and better characterize potential adverse events during spaceflight, ideally preventing them and maintaining astronauts wellness. Here we provide a time-resolved assessment of the impact of spaceflight on multiple astronauts (n=27) by studying multiple biochemical and immune measurements before, during, and after long-duration orbital spaceflight. We reveal space-associated changes of astronauts physiology on both the individual level and across astronauts, including associations with bone resorption and kidney function, as well as immune-system dysregulation.

molecular biology↗

bioRxiv2022

Community assessment of methods to deconvolve cellular composition from bulk gene expression

We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.

bioinformatics↗

bioRxiv2021

Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjects

Longitudinal deep multi-omics profiling, which combines biomolecular, physiological, environmental and clinical measures data, shows great promise for precision health. However, integrating and understanding the complexity of such data remains a big challenge. Here we propose a bottom-up framework starting from assessing single individuals multi-omics time series, and using individual responses to assess multi-individual grouping based directly on similarity of their longitudinal deep multi-omics profiles. We applied our method to individual profiles from a study profiling longitudinal responses in type 2 diabetes mellitus. After generating periodograms for individual subject omics signals, we constructed within-person omics networks and analyzed personal-level immune changes. The results showed that our method identified both individual-level responses to immune perturbation, and the clusters of individuals that have similar behaviors in immune response and which was associated to measures of their diabetic status.

genomics↗

bioRxiv2021

Differential Network Analysis of Longitudinal Gene Expression in Response to Perturbations.

Understanding changes in gene expression under the effects of a perturbation is a key goal of systems biology. A powerful approach to address this goal uses gene networks and describes the perturbations effects as a rewiring of each genes connections. This approach is known as differential network (DN) analysis. Here, we used DNs to analyze RNA-sequencing time series datasets, focusing on expression changes: (i) In the saliva of a human subject after vaccination with a pneumococcal vaccine (PPSV23), and (ii) in B cells treated ex vivo with a monoclonal antibody drug (Rituximab). Using network community detection, we revealed the collective behavior of clusters of genes, and detected communities of genes based on their longitudinal behavior, and corresponding pathway activations. We identified biological pathways consistent with the mechanism of action of the vaccine and with Rituximabs targets. The approach may be useful in drug development by providing an effective analysis of expressing changes in response to a drug.

bioinformatics↗

bioRxiv2021

Comberons from single cell transcriptomics in endothelial cells

VEGF inhibitor drugs have been successful, especially in ophthalmology, but not all patients respond to them. Combinations of drugs are likely to be needed for more effective therapies of angiogenesis-related diseases. In this paper we describe naturally occurring combinations of receptors in endothelial cells that might help to understand how cells communicate and to identify targets for drug combinations. We also develop and share a new software tool called DECNEO to identify them. Single-cell gene expression data are used to identify a set of co-expressed endothelial cell receptors, conserved among species (mice and humans) and enriched, within a network, of connections to up-regulated genes. This set includes several receptors previously shown to play a role in angiogenesis. Multiple statistical tests from large datasets, including an independent validation set, support the reproducibility, evolutionary conservation and role in angiogenesis of these naturally occurring combinations of receptors. We also show tissue-specific combinations and, in the case of choroid endothelial cells, consistency with both well-established and recent experimental findings, presented in a separate paper. The results and methods presented here advance the understanding of signaling to endothelial cells. The methods are generally applicable to the decoding of intercellular combinations of signals.

systems biology↗

bioRxiv2020

Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics

MotivationAnalysis of singe cell RNA sequencing (scRNA-seq) typically consists of different steps including quality control, batch correction, clustering, cell identification and characterization, and visualization. The amount of scRNA-seq data is growing extremely fast, and novel algorithmic approaches improving these steps are key to extract more biological information. Here, we introduce: (i) two methods for automatic cell type identification (i.e. without expert curator) based on a voting algorithm and a Hopfield classifier, (ii) a method for cell anomaly quantification based on isolation forest, and (iii) a tool for the visualization of cell phenotypic landscapes based on Hopfield energy-like functions. These new approaches are integrated in a software platform that includes many other state-of-the-art methodologies and provides a self-contained toolkit for scRNA-seq analysis. ResultsWe present a suite of software elements for the analysis of scRNA-seq data. This Python-based open source software, Digital Cell Sorter (DCS), consists in an extensive toolkit of methods for scRNA-seq analysis. We illustrate the capability of the software using data from large datasets of peripheral blood mononuclear cells (PBMC), as well as plasma cells of bone marrow samples from healthy donors and multiple myeloma patients. We test the novel algorithms by evaluating their ability to deconvolve cell mixtures and detect small numbers of anomalous cells in PBMC data. AvailabilityThe DCS toolkit is available for download and installation through the Python Package Index (PyPI). The software can be deployed using the Python import function following installation. Source code is also available for download on Zenodo: doi.org/10.5281/zenodo.2533377 Contactdomansk6@msu.edu

bioinformatics↗

bioRxiv2020

Longitudinal Saliva Omics Responses to Immune Perturbation: A Case Study

Saliva omics, a rapidly developing field for non-invasive diagnostics, may be utilized for monitoring very young or elderly populations, as well as individuals in remote locations. In this study, multiple saliva omics from an individual were monitored over 100 timepoints, over three periods involving: (i) hourly sampling over 24 hours without intervention, (ii) hourly sampling over 24 hours including immune system activation using the standard 23-valent pneumococcal polysaccharide vaccine, (iii) daily sampling for 33 days profiling the post-vaccination response. At each timepoint total saliva transcriptome and proteome were profiled, and salivary extracellular vesicles were derived, from which small-RNA sequencing was used to determine RNA, miRNA, piRNA and bacterial RNA components. The two 24-hour periods were used in a paired analysis to reveal vaccination responses. Temporal trends were classified and collective behavior revealed broad immune-responses captured in saliva, both at the innate as well as the adaptive response time frames.

genomics↗

bioRxiv2020

Visibility Graph Based Community Detection for Biological Time Series

MotivationTemporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems: (i) How to create the appropriate network to reflect the characteristics of biological time series. (ii) How to detect characteristic temporal patterns or events as network communities. General methods to detect communities have used metrics to compare the connectivity within a community to the connectivity one would expect in a random model, or assumed a known number of communities, or are based on the betweenness centrality of edges or nodes. However, such methods were not specifically designed for network representations of time series. We introduce a visibility-graph-based method to build networks from different kinds of biological time series and detect temporal communities within these networks. ResultsTo characterize the uneven sampling of typical experimentally obtained biological time series, and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG) for time series. To detect communities, we first find the shortest path of the network between start and end nodes to identify nodes which have high intensities. This identifies the main stem of our community detection algorithm. Then, we aggregate nodes outside the shortest path to the nodes found on the main stem based on the closest path length. Through simulation, we demonstrate the validity of our method in detecting community structures on various networks derived from simulated time series. We also confirm its effectiveness in revealing temporal communities in experimental biological time series. Our results suggest our method of visibility graph based community detection can be effective in detecting temporal biological patterns. AvailabilityThe methods of building WDPVG and visibility graph based community detection are available as a module of the open source Python package PyIOmica (https://doi.org/10.5281/zenodo.3691912) with documentation at https://pyiomica.readthedocs.io/en/latest/. The dataset and codes we used in this manuscript are publicly available at https://doi.org/10.5281/zenodo.3693984. Contactgmias@msu.edu

bioinformatics↗

bioRxiv2019

PyIOmica: Longitudinal Omics Analysis and Classification

SummaryPyIOmica is an open-source Python package focusing on integrating longitudinal multiple omics datasets, characterizing, and classifying temporal trends. The package includes multiple bioinformatics tools including data normalization, annotation, classification, visualization, and enrichment analysis for gene ontology terms and pathways. Additionally, the package includes an implementation of visibility graphs to visualize time series as networks.\n\nAvailability and implementationPyIOmica is implemented as a Python package (pyiomica), available for download and installation through the Python Package Index (PyPI) (https://pypi.python.org/pypi/pyiomica), and can be deployed using the Python import function following installation. PyIOmica has been tested on Mac OS X, Unix/Linux and Microsoft Windows. The application is distributed under an MIT license. Source code for each release is also available for download on Zenodo (https://doi.org/10.5281/zenodo.3342612).\n\nContactgmias@msu.edu

systems biology↗

bioRxiv2019

Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters

BackgroundSingle cell RNA sequencing (scRNA-seq) brings unprecedented opportunities for mapping the heterogeneity of complex cellular environments such as bone marrow, and provides insight into many cellular processes. Single cell RNA-seq, however, has a far larger fraction of missing data reported as zeros (dropouts) than traditional bulk RNA-seq. This makes difficult not only the clustering of cells, but also the assignment of the resulting clusters into predefined cell types based on known molecular signatures, such as the expression of characteristic cell surface markers. ResultsWe present a computational tool for processing single cell RNA-seq data that uses a voting algorithm to identify cells based on approval votes received by known molecular markers. Using a stochastic procedure that accounts for biases due to dropout errors and imbalances in the number of known molecular signatures for different cell types, the method computes the statistical significance of the final approval score and automatically assigns a cell type to clusters without an expert curator. We demonstrate the utility of the tool in the analysis of eight samples of bone marrow from the Human Cell Atlas. The tool provides a systematic identification of cell types in bone marrow based on a recently-published manually-curated cell marker database [1], and incorporates a suite of visualization tools that can be overlaid on a t-SNE representation. The software is freely available as a python package at https://github.com/sdomanskyi/DigitalCellSorter ConclusionsThis methodology assures that extensive marker to cell type matching information is taken into account in a systematic way when assigning cell clusters to cell types. Moreover, the method allows for a high throughput processing of multiple scRNA-seq datasets, since it does not involve an expert curator, and it can be applied recursively to obtain cell sub-types. The software is designed to allow the user to substitute the marker to cell type matching information and apply the methodology to different cellular environments.

bioinformatics↗

bioRxiv2017

Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

Modern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics. Next, we use a genetic algorithm to identify sets of genes which, when selectively inhibited by local external fields representing gene silencing compounds such as kinase inhibitors, disrupt the encoded cell cycle. We find, for example, that inhibiting the set of four kinases BRD4, MAPK1, NEK7, and YES1 in HeLa cells causes simulated cells to accumulate in the M phase. Finally, we suggest possible improvements and extensions to our model.\n\nAuthor SummaryCell cycle - the process in which a parent cell replicates its DNA and divides into two daughter cells - is an upregulated process in many forms of cancer. Identifying gene inhibition targets to regulate cell cycle is important to the development of effective therapies. Although modern high throughput techniques offer unprecedented resolution of the molecular details of biological processes like cell cycle, analyzing the vast quantities of the resulting experimental data and extracting actionable information remains a formidable task. Here, we create a dynamical model of the process of cell cycle using the Hopfield model (a type of recurrent neural network) and gene expression data from human cervical cancer cells and yeast cells. We find that the model recreates the oscillations observed in experimental data. Tuning the level of noise (representing the inherent randomness in gene expression and regulation) to the \"edge of chaos\" is crucial for the proper behavior of the system. We then use this model to identify potential gene targets for disrupting the process of cell cycle. This method could be applied to other time series data sets and used to predict the effects of untested targeted perturbations.

systems biology↗