Biology subjects

Srinivasan, C.

Publications and source records attributed to Srinivasan, C..

9 recordsLinked to original sources

bioRxiv2026

Fine-mapping candidate neuropsychiatric regulatory variants using cell type-aware comparative genomics

AbstractMeasures of nucleotide sequence conservation across species are useful for identifying functional genomic loci, but can fail when regulatory function is maintained, often in a cell type-specific manner, even when sequence is not. We introduce CTACIT, the Cell Type-Aware Conservation Inference Toolkit, to identify trait-associated regulatory variants. CTACIT integrates sequence conservation scores with cell type-specific open chromatin data collected from a few mammalian species to impute function for hundreds more. Applying CTACIT to neuropsychiatric trait loci identifies higher heritability enrichment and more fine-mapped variants than nucleotide conservation and human chromatin data alone. Our in vivo reporter assays validate predictions for enhancers with risk variants near the DRD2 schizophrenia risk locus. By integrating genome conservation and multi-species open chromatin data, CTACIT prioritizes variants within regions of conserved regulatory function for in vivo characterization and addresses a major challenge in translating disease associations to mechanistic understanding. One-Sentence SummaryRegulatory functions conserved across mammals underlying caudate cell type evolution reveal functions of human risk variants.

genomics↗

bioRxiv2025

Combining Machine Learning and Multiplexed, In Situ Profiling to Engineer Cell Type and Behavioral Specificity

A promising strategy for the precise control of neural circuits is to use cis-regulatory enhancers to drive transgene expression in specific cells. However, enhancer discovery faces key challenges: low in vivo success rates, species-specific differences in activity, challenges with multiplexing adeno-associated viruses (AAVs), and the lack of spatial detail from single-cell sequencing. In order to accelerate enhancer discovery for the dorsal spinal cord--a region critical for pain and itch processing--we developed an end-to-end platform, ESCargoT (Engineered Specificity of Cargo Transcription), combining machine learning (ML)-guided enhancer prioritization, modular AAV assembly, and multiplexed, in situ screening. Using cross-species chromatin accessibility data, we trained ML models to predict enhancer activity in oligodendrocytes and in 15 dorsal horn neuronal subtypes. We first demonstrated that an initial enhancer, Excit-1, targeted excitatory dorsal horn neurons and drove reversal of mechanical allodynia in an inflammatory pain model. To enable parallel profiling of a 27-enhancer-AAV library delivered intraspinally in mice, we developed a Spatial Parallel Reporter Assay (SPRA) by integrating a novel Golden-Gate assembly pipeline with multiplexed, in situ screening. Regression adjustment for spatial confounding enabled specificity comparisons between enhancers, demonstrating the ability to screen enhancers targeting diverse cell types (oligodendrocytes, motoneurons, dorsal neuron subtypes) in one experiment. We then validated two candidates, targeting Exc-LMO3 and Exc-SKOR2 neurons, respectively. In a companion paper by Noh et al, our colleagues show that the functional specificity of the Exc-SKOR2-targeting enhancer, unlike Excit-1, is capable of blocking the sensation of chemical itch in mice. These enhancers were derived from the macaque genome but displayed functional sensitivity in mice. This platform enables spatially resolved, multiplexed in vivo enhancer profiling to accelerate discovery of cell-targeting tools and gene therapy development.

neuroscience↗

bioRxiv2024

A three-dimensional histological cell atlas of the developing human brain

The human brain is believed to contain a full complement of neurons by the time of birth together with a substantial amount of the connectivity architecture, even though a significant amount of growth occurs postnatally. The developmental process leading to this outcome is not well understood in humans in comparison with model organisms. Previous magnetic resonance imaging (MRI) studies give three-dimensional coverage but not cellular resolution. In contrast, sparsely sampled histological or spatial omics analyses have provided cellular resolution but not dense whole brain coverage. To address the unmet need to provide a quantitative spatiotemporal map of developing human brain at cellular resolution, we leveraged tape-transfer assisted serial section histology to obtain contiguous histological series and unbiased imaging with dense coverage. Interleaved 20 thick Nissl and H&E series and MRI volumes are co-registered into multimodal reference volumes with 60 isotropic resolution, together with atlas annotations and a stereotactic coordinate system based on skull landmarks. The histological atlas volumes have significantly more contrast and texture than the MRI volumes. We computationally detect cells brain-wide to obtain quantitative characterization of the cytoarchitecture of the developing brain at 13-14 and 20-21 gestational weeks, providing the first comprehensive regional cell counts and characterizing the differential growth of the different brain compartments. Morphological characteristics permit segmentation of cell types from histology. We detected and quantified brain-wide distribution of mitotic figures representing dividing cells, providing an unprecedented spatiotemporal atlas of proliferative dynamics in the developing human brain. Further, we characterized the abundance and distribution of Cajal-Retzius cells, a transient cell population that plays essential roles in organizing glutamatergic cortical neurons into layers. Together, our study provides an unprecedented quantitative window into the developing human brain and the reference volumes and coordinate space should be useful for integrating spatial omics data sets with dense histological context.

neuroscience↗

bioRxiv2024

Cell Type Specific Enhancers for Dorsolateral Prefrontal Cortex.

The dorsolateral prefrontal cortex (DLPFC) is crucial to primate cognitive functions, but a paucity of cell type specific tools limits studies of DLPFC neurocomputational principles. Therefore, we set out to identify enhancers that fit inside Adeno-Associated Virus (AAV) vectors and that elicited functional, cell type specific gene expression in the non-human primate (NHP) DLPFC. We used single nucleus RNA-Seq and ATAC-Seq from rhesus macaque tissue samples to define DLPFC cell types and their associated open chromatin regions (OCRs). We trained machine learning (ML) models to recognize the unique regulatory grammar associated with each DLPFC neuron type, performed in silico screening of all OCRs, and identified candidate enhancers most likely to elicit cell type specific transgene expression in each neuron type. For layer 3 pyramidal neurons (L3PNs) and layer 5 extratelencephalic neurons (L5ETs), we cloned the top twelve identified candidates into AAVs and injected them into NHP DLPFC. In situ observation of enhancer-driven expression revealed the best performers, RMacL3-01 and RMacL5ET-01. We validated RMacL3-01 and RMacL5ET-01 using one-at-a-time injections in NHP DLPFC. RMacL3-01 restricted GFP expression to pyramidal neurons in layers 2 and 3, whereas RMacL5ET-01 restricted expression to POU3F1+ neurons in layer 5. RMacL3-01 elicited functional levels of channelrhodopsin expression that enabled optical activation of single- and multi-unit activity in NHP DLFPC. Together, these results and resources establish a solid foundation to study cell type specific principles of primate cognitive functions.

neuroscience↗

bioRxiv2023

Three-dimensional characterization of medium spiny neuron heterogeneity in the adult mouse striatum

Striatal projection neurons (SPNs) are traditionally segregated into two subpopulations expressing dopamine (DA) D1-like or D2-like receptors. However, this dichotomy is challenged by recent evidence. Functional and expression studies raise important questions: do SPNs co-express different DA receptors, and do these differences reflect unique striatal spatial distributions and expression profiles? Using RNAscope in mouse striatum, we report heterogenous SPN subpopulations distributed across dorsal-ventral and rostral-caudal axes. SPN subpopulations co-express multiple DA receptors, including D1 and D2 (D1/2R) and D1 and D3. Our integrative approach using single-nuclei multi-omics analyses provides a simple consensus to describe SPNs across diverse datasets, connecting it to complementary spatial mapping. Combining RNAscope and multi-omics shows D1/2R SPNs further separate into distinct subtypes according to spatial organization and conserved marker genes. Each SPN cell type contributes uniquely to genetic risk for neuropsychiatric diseases. Our results bridge anatomy and transcriptomics to offer new understandings of striatal neuron heterogeneity.

neuroscience↗

bioRxiv2022

Relating enhancer genetic variation across mammals to complex phenotypes using machine learning

Protein-coding differences between mammals often fail to explain phenotypic diversity, suggesting involvement of enhancers, often rapidly evolving regions that regulate gene expression. Identifying associations between enhancers and phenotypes is challenging because enhancer activity is context-dependent and may be conserved without much sequence conservation. We developed TACIT (Tissue-Aware Conservation Inference Toolkit) to associate open chromatin regions (OCRs) with phenotypes using predictions in hundreds of mammalian genomes from machine learning models trained to learn tissue-specific regulatory codes. Applying TACIT for motor cortex and parvalbumin-positive interneurons to neurological phenotypes revealed dozens of new OCR-phenotype associations. Many associated OCRs were near relevant genes, including brain size-associated OCRs near genes mutated in microcephaly or macrocephaly. Our work creates a forward genomics foundation for identifying candidate enhancers associated with phenotype evolution. One Sentence SummaryA new machine learning-based approach associates enhancers with the evolution of brain size and behavior across mammals.

genomics↗

bioRxiv2021

Molecular rhythm alterations in prefrontal cortex and nucleus accumbens associated with opioid use disorder

Severe and persistent disruptions to sleep and circadian rhythms are common features of people with opioid use disorder (OUD). Preclinical findings suggest altered molecular rhythms in the brain are involved in opioid reward and dependence. However, whether molecular rhythms are disrupted in brains of people with OUD remained an open question, critical to understanding the role of circadian rhythms in opioid addiction. We previously used subjects times of death (TOD) as a marker of time of day to investigate transcriptional rhythm alterations in psychiatric disorders. Using TOD and RNA sequencing, we discovered rhythmic transcripts in both the dorsolateral prefrontal cortex (DLPFC) and nucleus accumbens (NAc), key brain areas involved in opioid addiction, were largely distinct between OUD and unaffected comparison subjects. Further, fewer rhythmic transcripts were identified in DLPFC of OUD subjects compared to unaffected subjects, but nearly double the number of rhythmic transcripts were found in the NAc of OUD subjects. In OUD, rhythmic transcripts in the NAc peaked either in the evening or near sunrise, and were associated with dopamine, opioid, and GABAergic neurotransmission. Co-expression network analysis identified several OUD-specific modules in the NAc, enriched for transcripts involved in the modulation of dopamine and GABA synapses, including glutamatergic signaling and extracellular matrices. Integrative analyses with human GWAS revealed that rhythmic transcripts in DLPFC and NAc were enriched for genomic loci associated with sleep duration and insomnia. Overall, our results connect transcriptional rhythm changes in dopamine, opioid, and GABAergic synaptic signaling in human brain to sleep-related phenotypes and OUD.

neuroscience↗

bioRxiv2020

Addiction-associated genetic variants implicate brain cell type- and region-specific cis-regulatory elements in addiction neurobiology

Recent large genome-wide association studies (GWAS) have identified multiple confident risk loci linked to addiction-associated behavioral traits. Genetic variants linked to addiction-associated traits lie largely in non-coding regions of the genome, likely disrupting cis-regulatory element (CRE) function. CREs tend to be highly cell type-specific and may contribute to the functional development of the neural circuits underlying addiction. Yet, a systematic approach for predicting the impact of risk variants on the CREs of specific cell populations is lacking. To dissect the cell types and brain regions underlying addiction-associated traits, we applied LD score regression to compare GWAS to genomic regions collected from human and mouse assays for open chromatin, which is associated with CRE activity. We found enrichment of addiction-associated variants in putative CREs marked by open chromatin in neuronal (NeuN+) nuclei collected from multiple prefrontal cortical areas and striatal regions known to play major roles in reward and addiction. To further dissect the cell type-specific basis of addiction-associated traits, we also identified enrichments in human orthologs of open chromatin regions of mouse neuronal subtypes: cortical excitatory, D1, D2, and PV. Lastly, we developed machine learning models from mouse cell type-specific regions of open chromatin to further dissect human NeuN+ open chromatin regions into cortical excitatory or striatal D1 and D2 neurons and predict the functional impact of addiction-associated genetic variants. Our results suggest that different neuronal subtypes within the reward system play distinct roles in the variety of traits that contribute to addiction. Significance StatementWe combine statistical genetic and machine learning techniques to find that the predisposition to for nicotine, alcohol, and cannabis use behaviors can be partially explained by genetic variants in conserved regulatory elements within specific brain regions and neuronal subtypes of the reward system. This computational framework can flexibly integrate open chromatin data across species to screen for putative causal variants in a cell type-and tissue-specific manner across numerous complex traits.

genomics↗

bioRxiv2020

Transcriptional alterations in opioid use disorder reveal an interplay between neuroinflammation and synaptic remodeling

BackgroundPrevalence rates of opioid use disorder (OUD) have increased dramatically, accompanied by a surge of overdose deaths. While opioid dependence has been extensively studied in preclinical models, an understanding of the biological alterations that occur in the brains of people who chronically use opioids and who are diagnosed with OUD remains limited. To address this limitation, RNA-sequencing (RNA-seq) was conducted on the dorsolateral prefrontal cortex (DLPFC) and nucleus accumbens (NAc), regions heavily implicated in OUD, from postmortem brains in subjects with OUD. MethodsWe performed RNA-seq on the DLPFC and NAc from unaffected comparison subjects (n=20) and subjects diagnosed with OUD (n=20). Our transcriptomic analyses identified differentially expressed (DE) transcripts and investigated the transcriptional coherence between brain regions using rank-rank hypergeometric ordering (RRHO). Weighted gene co-expression analyses (WGCNA) also identified OUD-specific modules and gene networks. Integrative analyses between DE transcripts and GWAS datasets using linkage disequilibrium score (LDSC) assessed the genetic liability psychiatric-related phenotypes. ResultsRRHO analyses revealed extensive overlap in transcripts between DLPFC and NAc in OUD, primarily relating to synaptic remodeling and neuroinflammation. Identified transcripts were enriched for factors that control pro-inflammatory cytokine-mediated, chondroitin sulfate, and extracellular matrix signaling. Cell-type deconvolution implicated a role for microglia as a critical driver for opioid-induced neuroplasticity. Using LDSC, we discovered genetic liabilities for risky behavior, attention deficit hyperactivity disorder, and depression. ConclusionsOverall, our findings reveal new connections between the brains immune system and opioid dependence in the human brain.

neuroscience↗