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Sanchez-Fernandez, A.

Publications and source records attributed to Sanchez-Fernandez, A..

3 recordsLinked to original sources

Structural heterogeneity in mRNA-LNP subpopulations revealed by AF4-SAXS: implications for cargo loading and cell transfection

Lipid nanoparticles are the leading platform for the delivery of nucleic acid therapeutics, yet their structural complexity remains a significant barrier to achieve rational design and predictable function. Part of this complexity arises from the non-equilibrium assemblies that are difficult to identify using ensemble average techniques given the substantial heterogeneity in all properties. Aiming to overcome the limitations of traditional characterization methods, we combined asymmetric flow field-flow fractionation with in-line small-angle X-ray scattering and spectroscopic analyses, nanoflow cytometry, and cryo-EM to construct detailed structural models of mRNA-loaded nanoparticles formulated with different amounts of mRNA loading (N/P ratios of 3 and 6). This combination of techniques revealed that microfluidic formulation produces structurally diverse nanoparticle subpopulations differing in size, anisotropy, and cargo loading. Notably, these variations extend to the particle internal organization: spheroidal geometries display densely loaded mRNA cores, whereas bleb-like morphologies exhibit reduced mRNA content relative to the lipid amount within segregated domains at the core. NanoFCM further shows that the N/P ratio modulates cargo distribution across individual nanoparticles, with N/P=6 yielding a more uniform mRNA copy number per particle across subpopulations than N/P=3. These differences resulted in higher transfection efficacies for the N/P=6 formulation, highlighting core organization and loading homogeneity as key parameters for efficacious delivery. Together, these results establish a direct link between LNP architecture, internal organization, cargo distribution, and transfection efficiency, underscoring the importance of accounting for heterogeneity in the rational design of nucleic acid delivery systems.

biophysics↗

Single Nuclei-Derived Molecular Subtypes of Gastrointestinal Stromal Tumors Correlate with Clinicopathologic Features and Predict Clinical Outcomes

Historically, gastrointestinal stromal tumor (GIST) has been subtyped by oncogenic driver mutations. However, tumors with the same mutational profile can have variable biology. To further explore the impact of molecular diversity on GIST biology, we performed single nucleus RNA sequencing on 16 primary GIST and utilized an integrated single cell atlas of the normal GI tract composed from multiple publicly available datasets to identify six distinct GIST cell states. We then statistically estimated the relative abundances of these profiles in bulk transcriptomic data. These were used to define six common GIST molecular subtypes based upon one or two predominant tumor cell states. We found that these molecular subtypes correlate with tumor locations, mutational profiles, and patient outcomes, and validated these subtypes in an independent international cohort. These molecular subtypes have the potential to be used for clinical prognostication for patients with GIST, identifying new therapeutic targets, and studying the cell of transformation of GIST.

cancer biology↗

CLOOME: a new search engine unlocks bioimaging databases for queries with chemical structures

Currently, bioimaging databases cannot be queried by chemical structures that induce the phenotypic effects captured by an image. Through the advent of the contrastive learning paradigm, images and text could be embedded into the same space. We build on this contrastive learning paradigm, to present a novel retrieval system that is able to identify the correct bioimage given a chemical structure out of a database of[~] 2,000 candidate images with a top-1 accuracy >70 times higher than a random baseline. Additionally, the learned embeddings of our method are highly transferable to various relevant downstream tasks in drug discovery, including activity prediction, microscopy image classification and mechanism of action identification.

bioinformatics↗