Biology subjects

Cinquemani, S.

Publications and source records attributed to Cinquemani, S..

3 recordsLinked to original sources

bioRxiv2024

Spatially-resolved molecular sex differences at single cell resolution in the adult human hypothalamus

The hypothalamus contains multiple regions, including the ventromedial hypothalamus (VMH) and arcuate (ARC), which are responsible for sex-differentiated functions such as endocrine signaling, metabolism, and reproductive behaviors. While molecular, anatomic, and sex-differentiated features of rodent hypothalamus are well-established, much less is known about these regions in humans. Here we provide a spatially-resolved single cell atlas of sex-differentially expressed (sex-DE) genes in human VMH and ARC. We identify neuronal populations governing hypothalamus-specific functions, define their spatial distributions, and show increased retinoid pathway gene expression compared to rodents. Within VMH and ARC, we find correlated autosomal expression differences localized to ESR1/TAC3-expressing and CRHR2-expressing neurons, and extensive sex-DE of genes linked to sex-biased disorders including autism, depression, and schizophrenia. Our molecular mapping of disease associations to hypothalamic cell types with established roles in sex-divergent physiology and behavior provides insights into mechanistic bases of sex bias in neurodevelopmental and neuropsychiatric disorders.

genomics↗

bioRxiv2024

Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex

BackgroundCellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Computational methods for deconvolution have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or immunohistochemistry reference data. A major limitation in developing improved deconvolution algorithms has been the lack of integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue sample. Deconvolution algorithm performance has not yet been evaluated across different RNA extraction methods (cytosolic, nuclear, or whole cell RNA), different library preparation types (mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets. ResultsA rich multi-assay dataset was generated in postmortem human dorsolateral prefrontal cortex (DLPFC) from 22 tissue blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, bulk RNA-seq (across six library/extraction RNA-seq combinations), and RNAScope/Immunofluorescence (RNAScope/IF) for six broad cell types. The Mean Ratio method, implemented in the DeconvoBuddies R package, was developed for selecting cell type marker genes. Six computational deconvolution algorithms were evaluated in DLPFC and predicted cell type proportions were compared to orthogonal RNAScope/IF measurements. ConclusionsBisque and hspe were the most accurate methods, were robust to differences in RNA library types and extractions. This multi-assay dataset showed that cell size differences, marker genes differentially quantified across RNA libraries, and cell composition variability in reference snRNA-seq impact the accuracy of current deconvolution methods.

neuroscience↗

bioRxiv2020

A numerical study about the flight of the dragonfly: 2D gliding and 3D hovering regimes

The dragonflys ability of gliding and performing dexterous maneuvers during flight attracts the interest of scientists and engineers who aim at replicating its performances in micro air vehicles. The great efficiency of its flight is achieved thanks to the vortices generated by wing movements and thanks to the corrugations on their surfaces. The high freedom of control of each wing has been proved to be the secret behind the dragonfly capability to carry out incredible flight manoeuvers. The study presented in this paper analyzes two of the most common flight regimes of the dragonfly. Firstly, some CFD simulations of gliding are performed and drag and lift coefficients have been calculated, showing a good match with experimental data found in literature. Then, hovering has been studied using a methodology inspired to the Blade Element Momentum (BEM) theory, which is usually applied in the context of wind turbines design. The lift force calculated with this simulation corresponds to the weight of dragonfly, suggesting the correctness of this analysis.

bioengineering↗