@article {772, title = {SigPrimedNet: A Signaling-Informed Neural Network for scRNA-seq Annotation of Known and Unknown Cell Types.}, journal = {Biology (Basel)}, volume = {12}, year = {2023}, month = {2023 Apr 10}, abstract = {

Single-cell RNA sequencing is increasing our understanding of the behavior of complex tissues or organs, by providing unprecedented details on the complex cell type landscape at the level of individual cells. Cell type definition and functional annotation are key steps to understanding the molecular processes behind the underlying cellular communication machinery. However, the exponential growth of scRNA-seq data has made the task of manually annotating cells unfeasible, due not only to an unparalleled resolution of the technology but to an ever-increasing heterogeneity of the data. Many supervised and unsupervised methods have been proposed to automatically annotate cells. Supervised approaches for cell-type annotation outperform unsupervised methods except when new (unknown) cell types are present. Here, we introduce SigPrimedNet an artificial neural network approach that leverages (i) efficient training by means of a sparsity-inducing signaling circuits-informed layer, (ii) feature representation learning through supervised training, and (iii) unknown cell-type identification by fitting an anomaly detection method on the learned representation. We show that SigPrimedNet can efficiently annotate known cell types while keeping a low false-positive rate for unseen cells across a set of publicly available datasets. In addition, the learned representation acts as a proxy for signaling circuit activity measurements, which provide useful estimations of the cell functionalities.

}, issn = {2079-7737}, doi = {10.3390/biology12040579}, author = {Gundogdu, Pelin and Alamo, Inmaculada and Nepomuceno-Chamorro, Isabel A and Dopazo, Joaquin and Loucera, Carlos} } @article {765, title = {An SPM-Enriched Marine Oil Supplement Shifted Microglia Polarization toward M2, Ameliorating Retinal Degeneration in Mice.}, journal = {Antioxidants (Basel)}, volume = {12}, year = {2022}, month = {2022 Dec 30}, abstract = {

Retinitis pigmentosa (RP) is the most common inherited retinal dystrophy causing progressive vision loss. It is accompanied by chronic and sustained inflammation, including M1 microglia activation. This study evaluated the effect of an essential fatty acid (EFA) supplement containing specialized pro-resolving mediators (SPMs), on retinal degeneration and microglia activation in mice, a model of RP, as well as on LPS-stimulated BV2 cells. The EFA supplement was orally administered to mice from postnatal day (P)9 to P18. At P18, the electrical activity of the retina was examined by electroretinography (ERG) and innate behavior in response to light were measured. Retinal degeneration was studied via histology including the TUNEL assay and microglia immunolabeling. Microglia polarization (M1/M2) was assessed by flow cytometry, qPCR, ELISA and histology. Redox status was analyzed by measuring antioxidant enzymes and markers of oxidative damage. Interestingly, the EFA supplement ameliorated retinal dysfunction and degeneration by improving ERG recording and sensitivity to light, and reducing photoreceptor cell loss. The EFA supplement reduced inflammation and microglia activation attenuating M1 markers as well as inducing a shift to the M2 phenotype in mouse retinas and LPS-stimulated BV2 cells. It also reduced oxidative stress markers of lipid peroxidation and carbonylation. These findings could open up new therapeutic opportunities based on resolving inflammation with oral supplementation with SPMs such as the EFA supplement.

}, issn = {2076-3921}, doi = {10.3390/antiox12010098}, author = {Olivares-Gonz{\'a}lez, Lorena and Velasco, Sheyla and Gallego, Idoia and Esteban-Medina, Marina and Puras, Gustavo and Loucera, Carlos and Mart{\'\i}nez-Romero, Alicia and Pe{\~n}a-Chilet, Maria and Pedraz, Jos{\'e} Luis and Rodrigo, Regina} } @article {705, title = {SMN1 copy-number and sequence variant analysis from next-generation sequencing data.}, journal = {Hum Mutat}, volume = {41}, year = {2020}, month = {2020 12}, pages = {2073-2077}, abstract = {

Spinal muscular atrophy (SMA) is a severe neuromuscular autosomal recessive disorder affecting 1/10,000 live births. Most SMA patients present homozygous deletion of SMN1, while the vast majority of SMA carriers present only a single SMN1 copy. The sequence similarity between SMN1 and SMN2, and the complexity of the SMN locus makes the estimation of the SMN1 copy-number by next-generation sequencing (NGS) very difficult. Here, we present SMAca, the first python tool to detect SMA carriers and estimate the absolute SMN1 copy-number using NGS data. Moreover, SMAca takes advantage of the knowledge of certain variants specific to SMN1 duplication to also identify silent carriers. This tool has been validated with a cohort of 326 samples from the Navarra 1000 Genomes Project (NAGEN1000). SMAca was developed with a focus on execution speed and easy installation. This combination makes it especially suitable to be integrated into production NGS pipelines. Source code and documentation are available at https://www.github.com/babelomics/SMAca.

}, keywords = {Base Sequence, DNA Copy Number Variations, High-Throughput Nucleotide Sequencing, Humans, Reproducibility of Results, Software, Survival of Motor Neuron 1 Protein}, issn = {1098-1004}, doi = {10.1002/humu.24120}, author = {L{\'o}pez-L{\'o}pez, Daniel and Loucera, Carlos and Carmona, Rosario and Aquino, Virginia and Salgado, Josefa and Pasalodos, Sara and Miranda, Mar{\'\i}a and Alonso, {\'A}ngel and Dopazo, Joaquin} }