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Olivera, A.

Publications and source records attributed to Olivera, A..

2 recordsLinked to original sources

Tetraspanin positivity as a function of extracellular vesicle size measured by a modified immuno-TEM protocol

The identification of surface markers that correlate with specific subpopulations of extracellular vesicles (EVs) is important for EV identification, classification, purification, sorting, and functional analysis. Tetraspanins such as CD9, CD63 and CD81 were once considered to be universal markers of exosomes: small EVs released into the extracellular space when late endosomes / multivesicular bodies fuse with the plasma membrane. In contrast, plasma membrane-derived ectosomes (also called microvesicles) have a different biogenesis, were often regarded as being larger than exosomes, and display a different surface proteome. However, recent studies have shown that tetraspanins such as CD9 and CD81 are highly enriched on ectosomes derived from various sources. Thus, it is currently unclear how tetraspanin content correlates with specific EV subpopulations. Here, we present a modified immuno-TEM protocol that can be easily applied to heterogeneous EV populations comprising both small and large EVs (and presumably also a collection of exosomes and ectosomes). In EVs purified from U-2 OS cells by size-exclusion chromatography, we show that the percentage of particles positive for CD9 and CD81 is significantly higher in the subpopulation of EVs [≤] 100 nm (i.e., small EVs). These results also explain discrepancies in the size distribution profiles that we obtained using the same EV preparations by alternative single-vesicle characterization platforms such as nano flow cytometry and SP-IRIS / ExoView. The latter, when used to capture tetraspanin-positive particles, returns a population that is relatively small in size.

molecular biology↗

Peptipedia v2.0: A peptide sequence database and user-friendly web platform. A major update

In recent years, peptides have gained significant relevance due to their therapeutic properties. The surge in peptide production and synthesis has generated vast amounts of data, enabling the creation of comprehensive databases and information repositories. Advances in sequencing techniques and artificial intelligence have further accelerated the design of tailor-made peptides. However, leveraging these techniques requires versatile and continuously updated storage systems, along with tools that facilitate peptide research and the implementation of machine learning for predictive systems. This work introduces Peptipedia v2.0, one of the most comprehensive public repositories of peptides, supporting biotechnological research by simplifying peptide study and annotation. Peptipedia v2.0 has expanded its collection by over 45% with peptide sequences that have reported biological activities. The functional biological activity tree has been revised and enhanced, incorporating new categories such as cosmetic and dermatological activities, molecular binding, and anti-ageing properties. Utilizing protein language models and machine learning, more than 90 binary classification models have been trained, validated, and incorporated into Peptipedia v2.0. These models exhibit average sensitivities and specificities of 0.877 {+/-} 0.0530 and 0.873 {+/-}0.054, respectively, facilitating the annotation of more than 3.6 million peptide sequences with unknown biological activities, also registered in Peptipedia v2.0. Additionally, Peptipedia v2.0 introduces description tools based on structural and ontological properties and user-friendly machinelearning tools to facilitate the application of machine-learning strategies to study peptide sequences. Peptipedia v2.0 is accessible under the Creative Commons CC BY-NC-ND 4.0 license at https://peptipedia.cl/.

bioinformatics↗