Biology subjects

Izeta, A.

Publications and source records attributed to Izeta, A..

7 recordsLinked to original sources

bioRxiv2025

A novel approach for reliable differentiation of lymphatic endothelial progenitor cells in vitro

The lymphatic system plays critical roles in fluid homeostasis, immune regulation, and lipid transport, making its dysfunction a contributor to numerous pathological conditions. Lymphatic tissue engineering aims to develop therapeutic strategies for lymphatic regeneration and repair, depending on the availability of suitable cell sources for lymphatic endothelial cell (LEC) generation. Key cell sources explored for lymphatic tissue engineering include the dermis, bone marrow and stromal vascular fraction. To our knowledge, no existing differentiation protocols can accurately generate lymphatic endothelial progenitor cells (LEPCs). This study presents a novel in vitro differentiation protocol for generating LEPCs from stromal vascular fraction, dermis and bone marrow-derived mesenchymal stem cells (MSCs). The feasibility of using them as tissue sources was first evaluated leading to dermal cells exclusion due to low yield post-isolation, and highlighting the limited differentiation efficiency of bone marrow MSCs. However, the stromal vascular fraction emerged as the optimal source, exhibiting robust LEPCs differentiation and superior scalability. In vitro characterization confirmed that LEPCs maintain a lymphatic endothelial phenotype, exhibiting high migratory ability, robust angiogenic structure formation, and consistent expression of key lymphatic markers in both 2D and 3D cultures, as validated by RNA-Seq analysis. The protocol provides a consistent platform for studying lymphatic endothelial biology and potential applications in regenerative medicine and therapeutic angiogenesis.

bioengineering↗

bioRxiv2025

Unveiling the neuro-vascular interplay in the skeletal muscle in health, injury and disease

Neuromuscular junctions (NMJs) are complex multicellular structures that convey motor neuron-induced responses in the skeletal muscle. Their cellular composition is well characterized, but interactions between the different cell types and with the surrounding microenvironment remain underexplored. Here, by using a panel of newly discovered mouse cell lineage markers, single-cell RNA sequencing analyses and iDisco tissue clarification, we demonstrate the existence of contacts between adjacent NMJs through their kranocytes, assembling kranocyte network-like structures within the muscle interstitium, which is essential for regulating blood flow and supporting muscle regeneration. Indeed, kranocytes do contact with the surrounding microvasculature as well, both of them uncovering a previously unexplored, expansive interactive system. Moreover, unlike the robustness shown by their counterparts, the terminal Schwann cells, we strikingly observed that kranocytes rapidly detected stressful conditions, leading to structural changes and even the loss of their connections in response to acute damage, such as muscle injury or neuromuscular disease-induced denervation. Therefore, we propose kranocytes as the interactive sensory platforms of the NMJs, working in a perpendicular axis of nerve-transmission, as key NMJ-connectors by, interconnecting adjacent NMJs and interacting with the microvasculature and the microenvironment, and also as NMJ-sensors by detecting microenvironmental inputs. Together, our data postulate kranocytes as triple connectors of the nervous, musculoskeletal and vascular systems.

neuroscience↗

bioRxiv2024

Comparative proteomic analysis of the composition of decellularized extracellular matrix (dECM) and dECM-based inks as compared to the native tissue

Regenerative medicine and tissue engineering approaches based on the use of 3D-bioprinted decellularized extracellular matrix (dECM) present the advantage of a relatively biomolecule-rich matrix, which directs cell growth and differentiation in a tissue-specific manner. However, little is known about the composition changes that occur with standard processing of dECM-based inks. To characterize this process, six porcine tissues/tissue layers (artery, breast, dermis, epidermis, muscle and nerve) were independently decellularized via chemical, mechanical and enzymatic processes and the resulting dECMs formulated into biocompatible inks, to serve as source biomaterials for 3D printing. A comparative liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomic analysis was carried out for native tissue, decellularized and formulated ECMs, and the resulting complexity of the matrisome analyzed. A core matrisome was found to overlap in all decellularized tissues, as well as tissue-specific components that correlated with predicted functional (gene ontology-based) definitions. The proportion of collagens (mostly the 1 chains of collagen type I and III) increased in the final processing step (inks) as compared to the native ECM and dECM stages. Overall, a median of 55 matrisomal proteins (range 45-126) was detected in the dECM-derived inks. This complexity is far superior in terms of mimicking the composition of native tissue to non-dECM-based inks. Our results support the use of dECM-based inks and biomaterials in mimicking native tissue ECM complexity, demonstrating tissue-specific composition, which can improve future therapeutic approximations.

bioengineering↗

bioRxiv2024

A consensus single-cell transcriptomic atlas of dermal fibroblast heterogeneity

Single-cell RNA sequencing (scRNAseq) studies have unveiled large transcriptomic heterogeneity within both human and mouse dermal fibroblasts, but a consensus atlas that spans both species is lacking. Here, by studying 25 human and 9 mouse datasets through a semi-supervised procedure, we categorize 15 distinct human fibroblast populations across 5 main axes. Analysis of human fibroblast markers characteristic of each population suggested diverse functions, such as position-dependent ECM synthesis, association with immune responses or structural roles in skin appendages. Similarly, mouse fibroblasts were categorized into 17 populations across 5 axes. Comparison of mouse and human fibroblast populations highlighted similarities suggesting a degree of functional overlap, though nuanced differences were also noted: transcriptomically, human axes seem to segregate by function, while mouse axes seem to prioritize positional information over function. Importantly, addition of newer datasets did not significantly change the defined population structure. This study enhances our understanding of dermal fibroblast diversity, shedding light on species-specific distinctions as well as shared functionalities.

bioinformatics↗

bioRxiv2022

Age- or lifestyle-induced accumulation of genotoxicityis associated with a generalized shutdown of long genetranscription

A causative role for DNA damage as a molecular driver of aging has long been advocated. Transcription-blocking lesions (TBLs) accumulate with age in a stochastic manner. Thus, gene expression data might reflect the gene length-dependent accumulation of TBLs. Here we present an analysis of gene expression as a function of gene length in several independent single-cell RNA sequencing datasets of mouse and human aging. We found a pervasive age-associated downregulation of long gene expression, which is seen across species, datasets, sexes, tissues and cell types. Furthermore, long gene downregulation was also observed in premature aging models such as UV-radiation and smoke exposure, and in gene expression data from progeroid diseases Cockayne syndrome and trichothiodystrophy. Finally, we analyzed the length of differentially expressed genes associated to age in both mice and humans. Downregulated genes were significantly longer than upregulated genes. These data highlight a previously undetected hallmark of cellular aging and provide strong support for age-associated accumulation of genotoxic damage inducing a generalized shutdown of RNA polymerase II-mediated long gene transcription. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/501099v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@1897c08org.highwire.dtl.DTLVardef@12e202borg.highwire.dtl.DTLVardef@141f70borg.highwire.dtl.DTLVardef@1c1f9f9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Lack of evidence for increased transcriptional noise in aged tissues

Aging is often associated with a loss of cell type identity that results in an increase in transcriptional noise in aged tissues. If this phenomenon reflects a fundamental property of aging remains an open question. Transcriptional changes at the cellular level are best detected by single-cell RNA sequencing (scRNAseq). However, the diverse computational methods used for the quantification of age-related loss of cellular identity have prevented reaching meaningful conclusions by direct comparison of existing scRNAseq datasets. To address these issues we created Decibel, a Python toolkit that implements side-to-side four commonly used methods for the quantification of age-related transcriptional noise in scRNAseq data. Additionally, we developed Scallop, a novel computational method for the quantification of membership of single cells to their assigned cell type cluster. Cells with a greater Scallop membership score are transcriptionally more stable. Application of these computational tools to seven aging datasets showed large variability between tissues and datasets, suggesting that increased transcriptional noise is not a universal hallmark of aging. To understand the source of apparent loss of cell type identity associated with aging, we analyzed cell type-specific changes in transcriptional noise and the changes in cell type composition of the mammalian lung. No robust pattern of cell type-specific transcriptional noise alteration was found across aging lung datasets. In contrast, age-associated changes in cell type composition of the lung were consistently found, particularly of immune cells. These results suggest that claims of increased transcriptional noise of aged tissues should be reformulated.

bioinformatics↗

bioRxiv2021

Triku: a feature selection method based on nearest neighbors for single-cell data

Feature selection is a relevant step in the analysis of single-cell RNA sequencing datasets. Triku is a feature selection method that favours genes defining the main cell populations. It does so by selecting genes expressed by groups of cells that are close in the nearest neighbor graph. Triku efficiently recovers cell populations present in artificial and biological benchmarking datasets, based on mutual information and silhouette coefficient measurements. Additionally, gene sets selected by triku are more likely to be related to relevant Gene Ontology terms, and contain fewer ribosomal and mitochondrial genes. Triku is available at https://gitlab.com/alexmascension/triku.

bioinformatics↗