Biology subjects

Slonim, D. K.

Publications and source records attributed to Slonim, D. K..

7 recordsLinked to original sources

bioRxiv2026

flashscenic: Accelerated GRN Analysis with RegDiffusion for Large-Scale Single-Cell Data

Single-cell gene regulatory network (GRN) analysis via the SCENIC framework provides biologically interpretable cell-state representations through motif-validated transcription factor (TF) regulon activities. However, sequential, CPU-bound implementations (pySCENIC) constrain scalability to highly variable genes and make the repeated executions needed to resolve network stochasticity computationally prohibitive. Furthermore, while regulon activity is hypothesized to mitigate technical batch effects, its integration performance has never been quantitatively assessed against modern data integration benchmarks. We present flashscenic, an end-to-end GPU-accelerated framework that resolves these bottlenecks and expands the practical scope of single-cell regulatory genomics. By reformulating module construction, cisTarget motif pruning, and AUCell scoring into batched GPU tensor operations, flashscenic achieves a 12- to 27-fold downstream speedup over pySCENIC, while maintaining strict numerical equivalence (Pearson r = 0.9996). Leveraging this throughput, we conducted the first systematic benchmark of nine consensus aggregation strategies across multiple independent tissue cohorts, demonstrating that discrete post-pruning recurrence voting ([≥]50% recurrence, [≥]5 targets per regulon) maximizes biological fidelity. Finally, data integration benchmarking across a 116,043-cell acute myeloid leukemia (AML) atlas quantitatively demonstrates that uncorrected TF activity provides an intrinsic, unsupervised batch-integration benefit over raw PCA. Combining it with a standard batch integration method matches or outperforms canonical expression-based integration while accurately recovering lineage-defining and oncogenic regulators (CEBPA, HOXA9). flashscenic is available via pip install flashscenic, with source code and tutorials at https://github.com/haozhu233/flashscenic.

bioinformatics↗

bioRxiv2023

Hofbauer cells and fetal brain microglia share transcriptional profiles and responses to maternal diet-induced obesity

Maternal immune activation is associated with adverse offspring neurodevelopmental outcomes, many mediated by in utero microglial programming. As microglia remain inaccessible throughout development, identification of noninvasive biomarkers reflecting fetal brain microglial programming could permit screening and intervention. We used lineage tracing to demonstrate the shared ontogeny between fetal brain macrophages (microglia) and fetal placental macrophages (Hofbauer cells) in a mouse model of maternal diet-induced obesity, and single-cell RNA-seq to demonstrate shared transcriptional programs. Comparison with human datasets demonstrated conservation of placental resident macrophage signatures between mice and humans. Single-cell RNA-seq identified common alterations in fetal microglial and Hofbauer cell gene expression induced by maternal obesity, as well as sex differences in these alterations. We propose that Hofbauer cells, which are easily accessible at birth, provide novel insights into fetal brain microglial programs, and may facilitate the early identification of offspring vulnerable to neurodevelopmental disorders in the setting of maternal exposures.

developmental biology↗

bioRxiv2023

From Noise to Knowledge: Probabilistic Diffusion-Based Neural Inference of Gene Regulatory Networks

AO_SCPLOWBSTRACTC_SCPLOWUnderstanding gene regulatory networks (GRNs) is crucial for elucidating cellular mechanisms and advancing therapeutic interventions. Original methods for GRN inference from bulk expression data often struggled with the high dimensionality and inherent noise in the data. Here we introduce RegDiffusion, a new class of Denoising Diffusion Probabilistic Models focusing on the regulatory effects among feature variables. RegDiffusion introduces Gaussian noise to the input data following a diffusion schedule and uses a neural network with a parameterized adjacency matrix to predict the added noise. We show that using this process, GRNs can be learned effectively with a surprisingly simple model architecture. In our benchmark experiments, RegDiffusion shows superior performance compared to several baseline methods in multiple datasets. We also demonstrate that RegDiffusion can infer biologically meaningful regulatory networks from real-world single-cell data sets with over 15,000 genes in under 5 minutes. This work not only introduces a fresh perspective on GRN inference but also highlights the promising capacity of diffusion-based models in the area of single-cell analysis. The RegDiffusion software package and experiment data are available at https://github.com/TuftsBCB/RegDiffusion.

bioinformatics↗

bioRxiv2021

Single cell profiling of Hofbauer cells and fetal brain microglia reveals shared programs and functions

Maternal immune activation is associated with adverse offspring neurodevelopmental outcomes, many of which are mediated by in utero microglial programming. Microglia remain inaccessible at birth and throughout development, thus identification of noninvasive biomarkers that can reflect fetal brain microglial programming may permit screening and intervention during critical developmental windows. Here we used lineage tracing to demonstrate the shared ontogeny between fetal brain macrophages (microglia) and fetal placental macrophages (Hofbauer cells). Single-cell RNA sequencing of murine fetal brain and placental macrophages demonstrated shared transcriptional programs. Comparison with human datasets demonstrated that placental resident macrophage signatures are highly conserved between mice and humans. Single-cell RNA-seq identified sex differences in fetal microglial and Hofbauer cell programs, and robust differences between placenta-associated maternal macrophage/monocyte (PAMM) populations in the context of a male versus a female fetus. We propose that Hofbauer cells, which are easily accessible at birth, provide novel insights into fetal brain microglial programs, potentially facilitating the early identification of offspring most vulnerable to neurodevelopmental disorders.

developmental biology↗

bioRxiv2019

TEMPO: Detecting Pathway-Specific Temporal Dysregulation of Gene Expression in Disease

While many transcriptional profiling experiments measure dynamic processes that change over time, few include enough time points to adequately capture temporal changes in expression. This is especially true for data from human subjects, for which relevant samples may be hard to obtain, and for developmental processes where dynamics are critically important. Although most expression data sets sample at a single time point, it is possible to use accompanying temporal information to create a virtual time series by combining data from different individuals.\n\nWe introduce TEMPO, a pathway-based outlier detection approach for finding pathways showing significant temporal changes in expression patterns from such combined data. We present findings from applications to existing microarray and RNA-seq data sets. TEMPO identifies temporal dysregulation of biologically relevant pathways in patients with autism spectrum disorders, Huntingtons disease, Alzheimers disease, and COPD. Its findings are distinct from those of standard temporal or gene set analysis methodologies.\n\nOverall, our experiments demonstrate that there is enough signal to overcome the noise inherent in such virtual time series, and that a temporal pathway approach can identify new functional, temporal, or developmental processes associated with specific phenotypes.\n\nAvailabilityAn R package implementing this method and full results tables are available at bcb.cs.tufts.edu/tempo/.

bioinformatics↗

bioRxiv2018

Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases

Identification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of gene and protein networks remains poorly understood. We launched the "Disease Module Identification DREAM Challenge", an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology, and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies (GWAS). Our critical assessment of 75 contributed module identification methods reveals novel top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets and correctly prioritize candidate disease genes. This community challenge establishes benchmarks, tools and guidelines for molecular network analysis to study human disease biology (https://synapse.org/modulechallenge).

bioinformatics↗

bioRxiv2017

Pathway centrality in protein interaction networks identifies functional mediators of pulmonary disease

Identification of functional pathways mediating molecular responses may lead to better understanding of disease processes and suggest new therapeutic approaches. We introduce a method to detect such mediating functions using topological properties of protein-protein interaction networks. We introduce the concept of pathway centrality, a measure of communication between disease genes and differentially expressed genes. We find mediating pathways for three pulmonary diseases (asthma; bronchopulmonary dysplasia (BPD); and chronic obstructive pulmonary disease (COPD)) using pathway centrality. Mediating pathways shared by all three pulmonary disorders heavily favor inflammatory or immune responses and include specific pathways such as cytokine production, NF Kappa B, and JAK/STAT signaling. Disease-specific mediators, such as insulin signaling in BPD or homeostasis in COPD, are also highlighted. We support our findings, some of which suggest new treatment approaches, both with anecdotal evidence from the literature and via systematic evaluation using genetic interactions.

bioinformatics↗