Biology subjects

Arbabi, K.

Publications and source records attributed to Arbabi, K..

7 recordsLinked to original sources

bioRxiv2026

brisc: a multithreaded CPU toolkit for single-cell analysis

Single-cell RNA sequencing datasets have grown from tens of thousands of cells to tens of millions. At this scale, standard toolkits like Scanpy and Seurat need hours per analysis, hindering iterative exploration. We present brisc, the first end-to-end multithreaded CPU toolkit for single-cell analysis, spanning dimensionality reduction, integration, label transfer, clustering, embedding, differential expression and plotting. Unlike GPU-based counterparts, brisc strictly guarantees reproducibility: identical results between runs, regardless of thread count. Algorithmic innovations include a blocked inverted-file-index-based nearest-neighbors algorithm that makes 81-98% fewer errors than Scanpy and Seurat. On a 192-CPU node, brisc ran a standard workflow on 10 million cells in 3 minutes, 116x faster than Scanpy at under half its peak memory usage and 236x faster than Seurat with BPCells. On 96 CPUs, it matched the speed of rapids-singlecell on four H100 GPUs. brisc enables near-real-time, reproducible analysis of tens of millions of cells without specialized hardware.

bioinformatics↗

bioRxiv2026

Selective depletion of upper-layer somatostatin interneuron subtypes in schizophrenia

Schizophrenia (SCZ) is associated with cortical GABAergic dysfunction, but whether inhibitory interneurons are lost or persist in an altered molecular state remains unresolved. Here, we harmonized seven prefrontal post-mortem single-nucleus RNA-seq datasets onto a fine-grained taxonomy of cortical cell types and meta-analyzed their gene expression and abundance changes in SCZ (298 controls, 171 SCZ). First, we find a subclass-wide reduction of SST mRNA within somatostatin (Sst) neurons. Second, we find reduced abundance (depletion) of a subset of upper-layer Sst interneurons and increased abundance of L6b excitatory neurons, with both changes confirmed in spatial transcriptomics (12 controls, 12 SCZ). Notably, SCZ genetic risk is enriched in the most depleted Sst cells. Depleted Sst subtypes highly express HCN1, partially correspond to primate-specialized CALB1-expressing double-bouquet cells, and are among the cells lost earliest in Alzheimer's disease. These upper-layer Sst interneurons constitute an intrinsically vulnerable population and a promising target for neuroprotective and compensatory therapies.

bioinformatics↗

bioRxiv2026

Spatial dynamics of cellular and molecular plasticity in the maternal and postpartum mouse brain

Pregnancy is a critical window for neuroplasticity and maternal mental health, yet our understanding of the molecular and cellular changes underlying this adaptation remains incomplete. Here we profile the female mouse brain in nulliparous, late-pregnant and postpartum states with three single-cell spatial technologies (Slide-tags, MERFISH and Xenium). Together these assays resolve 1.5 million cells in one coronal plane spanning the cortex, striatum, lateral septum and preoptic area. Pregnancy is associated with changes in gene expression in most cell types, beyond the circuits previously established to govern maternal behavior. These changes resolve into three programs that are reproducible across platforms: synaptic pathways increase in neurons as growth and plasticity pathways decrease; immune and angiogenic pathways increase in glia and vascular cells; and cholesterol synthesis decreases as uptake increases across both neurons and glia. Finally, mapping human depression genetics onto these data, we find risk genes concentrated almost entirely in neurons, and this concentration changes with reproductive state in the preoptic area, basal forebrain and ventral striatum. These results place the neurons carrying depression risk among the circuits remodeled during pregnancy, providing a possible cellular substrate for peripartum vulnerability.

genomics↗

bioRxiv2025

Hyperpolarization activated cation channel mediated intrinsic plasticity changes underlie the malleability of with-in cell-type electrophysiological heterogeneity

Within cell-type neuronal electrophysiological, morphological, and transcriptomic heterogeneity is the norm in the brain. Although generally considered a fixed property within cell-types, this heterogeneity is malleable and declines in regions of the human brain that generate seizures. Building off this foundational work we hypothesize that such plasticity of cell-type heterogeneity, specifically its decline, arises from the shared history of neuronal activity that drive intrinsic plasticity mechanisms in a concerted fashion. To explore this hypothesis we study neuronal activity in two model systems: human cortical slice cultures from patients with epilepsy as well as slices from the medial prefrontal cortex (mPFC) and subiculum of rodent kainic acid (KA) model of temporal lobe epilepsy. Biophysical properties and spiking dynamics were characterized using whole-cell patch clamp recordings of layer 2 and layer 3 (L2&3) pyramidal neurons in human slice culture as well as deep layer subicular neurons and layer 5 (L5) mPFC of KA mice. We found a significant decline in biophysical heterogeneity and a reduction in information coding in both the KA and slice culture models. In both these models we found a consistent increase in hyperpolarization-activated cation current (HCN) dependent electrophysiological properties, the blockade of which restored electrophysiological heterogeneity and information coding. Our findings demonstrate that within cell-type heterogeneity is malleable, and despite being a complex distributed network property, can be tuned by a single ionic current. These findings emphasize the plasticity of within cell-type heterogeneity, suggesting the potential for targeted interventions to restore neuronal heterogeneity changes that accompany epilepsy and potentially other neurological and neuropsychiatric diseases.

neuroscience↗

bioRxiv2024

Perinatal Brain Injury Triggers Niche-Specific Changes to Cellular Biogeography

Preterm infants are at risk for brain injury and neurodevelopmental impairment due, in part, to white matter injury following chronic hypoxia exposure. However, the precise molecular mechanisms by which perinatal hypoxia disrupts early neurodevelopment are poorly understood. Here, we constructed a brain-wide map of the regenerative response to newborn brain injury using high-resolution imaging-based spatial transcriptomics to analyze over 1.3 million cells in a mouse model of chronic neonatal hypoxia. Additionally, we developed a new method for inferring condition-associated differences in cell type spatial proximity, enabling the identification of niche-specific changes in cellular architecture. We observed hypoxia-associated changes in region-specific cell states, cell type composition, and spatial organization. Importantly, our analysis revealed mechanisms underlying reparative neurogenesis and gliogenesis, while also nominating pathways that may impede circuit rewiring following perinatal hypoxia. Altogether, our work provides a comprehensive description of the molecular response to newborn brain injury.

neuroscience↗

bioRxiv2022

Investigating microglia-neuron crosstalk by characterizing microglial contamination in human and mouse Patch-seq datasets

Microglia are dynamic immune cells with diverse functional roles, including the regulation of neuronal excitability. Here, we leveraged an inconvenient truth of neuronal Patch-seq datasets -- that they routinely display evidence of contamination by surrounding microglia -- to better understand aspects of microglia-neuronal crosstalk. We first quantified the presence of microglial transcripts in three Patch-seq datasets of human and mouse neocortical neurons and observed extensive off-target contamination by microglia in each. Variation in microglial contamination was explained foremost by donor identity, especially in human samples, and neuronal cell type identity. Differential expression testing and enrichment analyses suggest that microglial contamination in Patch-seq is reflective of activated microglia and that these transcriptional signatures are distinct from those captured via single-nucleus RNAseq. Finally, neurons with greater microglial contamination differed markedly in their electrophysiological characteristics, including lowered input resistances and more depolarized action potential thresholds. Our results suggest microglial contamination contributes to cell- and donor-related electrophysiological variability and sheds light on how microglia might impact neurons in vivo.

neuroscience↗

bioRxiv2021

A Practical Guide to Sparse Clustering for Studying Molecular Development of the Human Brain

Studying the molecular development of the human brain presents unique challenges for selecting a data analysis approach. The rare and valuable nature of human postmortem brain tissue, especially for developmental studies, means the sample sizes are small (n), but the use of high throughput genomic and proteomic methods measure the expression levels for hundreds or thousands of variables (e.g. genes or proteins (p)) for each sample. This leads to a data structure that is high dimensional (p >> n) and introduces the curse of dimensionality, which poses a challenge for traditional statistical approaches. In contrast, high dimensional analyses, especially cluster analyses developed for sparse data, have worked well for analyzing genomic datasets where p >> n. Here we explore applying a lasso-based clustering method developed for high dimensional genomic data with small sample sizes. Using protein and gene data from the developing human visual cortex, we compared clustering methods. We identified an application of sparse K-means clustering (Robust Sparse K-means Clustering (RSKC)) that partitioned samples into age-related clusters that reflect lifespan stages from birth to aging. RSKC adaptively selects a subset of the genes or proteins contributing to partitioning samples into age-related clusters that progress across the lifespan. This approach addresses a problem in current studies that could not identify multiple postnatal clusters. Moreover, clusters encompassed a range of ages like a series of overlapping waves illustrating that chronological- and brain-age have a complex relationship. In addition, a recently developed workflow to create plasticity phenotypes (Balsor et al., 2020) was applied to the clusters and revealed neurobiologically relevant features that identified how the human visual cortex changes across the lifespan. These methods can help address the growing demand for multimodal integration, from molecular machinery to brain imaging signals, to understand the human brains development.

neuroscience↗