Biology subjects

Saldi, G.-A.

Publications and source records attributed to Saldi, G.-A..

6 recordsLinked to original sources

bioRxiv2026

Rhythmic expression of a cAMP phosphodiesterase switches neuronal excitability states in the mammalian suprachiasmatic nucleus

How the transcriptome of a neuron determines its electrophysiological properties and how this change in different contexts are major questions in neurobiology. The intracellular molecular clocks in the circadian pacemaker neurons in the mammalian suprachiasmatic nucleus (SCN) drive 24hr rhythms in clock gene expression via a transcriptional-translational feedback loop1,2. These molecular clocks also control 24hr rhythms in the firing of SCN neurons3-5 - a subset of which enter a silent depolarized state around midday6. We used single-cell RNA sequencing and identified that the Avp / Nms and Vip / Nms are the SCN neurons that undergo daytime silencing. We also found high level and rhythmic expression of the Pde10a phosphodiesterase in these Nms SCN neurons. Acutely inhibiting Pde10a activity or elevating intracellular cAMP - to bypass high Pde10a levels - specifically shifts these Nms+ SCN neurons into a depolarized silent state hours ahead of schedule. A similar mechanism may occur in other neurons that switch to silent depolarized states.

neuroscience↗

bioRxiv2025

Distinct depressive-like behavioural phenotypes in mice exhibit unique patterns of transcriptional perturbations across habenular cell subtypes

Major depressive disorder arises from diverse neurobiological mechanisms, yet how specific habenular cell types contribute to distinct depressive-like behaviours remains unclear. Using chronic social defeat stress, behavioural phenotyping, and single-cell RNA sequencing, we mapped stress-induced transcriptional changes across habenular cell types and lateral habenula (LHb) subregions. Our findings reveal that each behavioural phenotype is marked by its own transcriptional profile. These subregion-specific patterns across the LHb suggest that different cellular circuits contribute uniquely to depressive-like states.

neuroscience↗

bioRxiv2022

Cortical somatostatin interneuron subtypes form cell-type specific circuits

The cardinal interneuron classes are a useful simplification of cortical interneuron diversity, but such broad subgroupings glosses over the molecular, morphological, and circuit specificity of interneuron subtypes, most notably among the somatostatin interneuron class. The organizing principles by which the connectivity of these subtypes is specified are unknown. To address this knowledge gap, we designed a series of genetic strategies to target the breadth of somatostatin interneuron subtypes. Using these strategies to target three subtypes that span the entire cortical column, we examined their afferent and efferent connectivity. Our data demonstrated that each of these possesses remarkable reciprocal connectivity with the intracortical or corticofugal pyramidal classes, as well as parvalbumin interneurons. Even when two interneuron subtypes shared the same efferent target, their synaptic targeting proved selective for particular dendritic compartments. We thus provide evidence that subtypes of somatostatin cortical interneurons form cell-type specific cortical circuits.

neuroscience↗

bioRxiv2021

Single-cell gene regulatory network inference atscale: The Inferelator 3.0

MotivationGene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above. ResultsIn this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator is able to integrate the largest single-cell datasets and learn cell-type specific gene regulatory networks. Compared to other network inference methods, the Inferelator learns new and informative Saccharomyces cerevisiae networks from single-cell gene expression data, measured by recovery of a known gold standard. We demonstrate its scaling capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data. AvailabilityThe inferelator software is available on GitHub (https://github.com/flatironinstitute/inferelator) under the MIT license and has been released as python packages with associated documentation (https://inferelator.readthedocs.io/).

systems biology↗

bioRxiv2020

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data

The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for noise. In spite of such efforts, no consensus on best practices has been established and all current approaches vary substantially based on the available data and empirical tests. The k-Nearest Neighbor Graph (kNN-G) is often used to infer the identities of, and relationships between, cells and is the basis of many widely used dimensionality-reduction and projection methods. The kNN-G has also been the basis for imputation methods using, e.g., neighbor averaging and graph diffusion. However, due to the lack of an agreed-upon optimal objective function for choosing hyperparameters, these methods tend to oversmooth data, thereby resulting in a loss of information with regard to cell identity and the specific gene-to-gene patterns underlying regulatory mechanisms. In this paper, we investigate the tuning of kNN- and diffusion-based denoising methods with a novel non-stochastic method for optimally preserving biologically relevant informative variance in single-cell data. The framework, Denoising Expression data with a Weighted Affinity Kernel and Self-Supervision (DEWAKSS), uses a self-supervised technique to tune its parameters. We demonstrate that denoising with optimal parameters selected by our objective function (i) is robust to preprocessing methods using data from established benchmarks, (ii) disentangles cellular identity and maintains robust clusters over dimension-reduction methods, (iii) maintains variance along several expression dimensions, unlike previous heuristic-based methods that tend to oversmooth data variance, and (iv) rarely involves diffusion but rather uses a fixed weighted kNN graph for denoising. Together, these findings provide a new understanding of kNN- and diffusion-based denoising methods and serve as a foundation for future research. Code and example data for DEWAKSS is available at https://gitlab.com/Xparx/dewakss/-/tree/Tjarnberg2020branch.

bioinformatics↗

bioRxiv2019

Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for transcriptionally barcoding gene deletion mutants and performing scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse genotypes in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We developed, and benchmarked, a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,018 interactions. Our study establishes a general approach to gene regulatory network reconstruction from scRNAseq data that can be employed in any organism.

genomics↗