Biology subjects

Emani, P. S.

Publications and source records attributed to Emani, P. S..

6 recordsLinked to original sources

bioRxiv2024

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized [~]250 disease-risk genes and drug targets with associated cell types. Summary Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=95 SRC="FIGDIR/small/585576v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@70b24org.highwire.dtl.DTLVardef@6c9eddorg.highwire.dtl.DTLVardef@131fae2org.highwire.dtl.DTLVardef@4e0f1e_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2023

Dynamic clustering of genomics cohorts beyond race, ethnicity--and ancestry

BackgroundRecent decades have witnessed a steady decrease in the use of race categories in genomic studies. While studies that still include race categories vary in goal and type, these categories already build on a history during which racial color lines have been enforced and adjusted in the service of social and political systems of power and disenfranchisement. For early modern biological classification systems, data collection was also considerably arbitrary and limited. Fixed, discrete classifications have limited the study of human biodiversity and disrupted widely spread genetic and phenotypic continuums across geographic scales. Relatedly, the use of broad and predefined classification schemes--e.g. continent-based--across traits can risk missing important trait-specific genomic signals. ResultsTo address these issues, we introduce a dynamic approach to clustering human genomics cohorts on a trait-specific level and without using a set of predefined categories. We tested the approach on whole-exome sequencing datasets in ten cancer types and partitioned them based on germline variants in cancer-relevant genes that could confer cancer type-specific disease predisposition. Results demonstrate clustering patterns that transcend discrete continent-based categories across cancer types. Functional analysis based on cancer type-specific clusterings also captures the fundamental biological processes underlying cancer, differentiates between dynamic clusters on a functional level, and identifies novel potential drivers overlooked by a continent-based clustering model. ConclusionsThrough a trait-based lens, the dynamic clustering approach reveals genomic patterns that transcend predefined classification categories. We propose that coupled with diverse data collection, new clustering approaches have the potential to draw a more complete portrait of genomic variation and to address, in parallel, technical and social aspects of studying human biodiversity.

genomics↗

bioRxiv2023

The Cellular Underpinnings of the Human Cortical Connectome

The functional properties of the human brain arise, in part, from the vast assortment of cell types that pattern the cortex. The cortical sheet can be broadly divided into distinct networks, which are further embedded into processing streams, or gradients, that extend from unimodal systems through higher-order association territories. Here, using transcriptional data from the Allen Human Brain Atlas, we demonstrate that imputed cell type distributions are spatially coupled to the functional organization of cortex, as estimated through fMRI. Cortical cellular profiles follow the macro-scale organization of the functional gradients as well as the associated large-scale networks. Distinct cellular fingerprints were evident across networks, and a classifier trained on post-mortem cell-type distributions was able to predict the functional network allegiance of cortical tissue samples. These data indicate that the in vivo organization of the cortical sheet is reflected in the spatial variability of its cellular composition.

neuroscience↗

bioRxiv2022

Insights from Incorporating Quantum Computing into Drug Design Workflows

While many quantum computing (QC) methods promise theoretical advantages over classical counterparts, quantum hardware remains limited. Exploiting near-term QC in computer-aided drug design (CADD) thus requires judicious partitioning between classical and quantum calculations. We present HypaCADD, a hybrid classical-quantum workflow for finding ligands binding to proteins, while accounting for genetic mutations. We explicitly identify modules of our drug design workflow currently amenable to replacement by QC: non-intuitively, we identify the mutation-impact predictor as the best candidate. HypaCADD thus combines classical docking and molecular dynamics with quantum machine learning (QML) to infer the impact of mutations. We present a case study with the SARS-CoV-2 protease and associated mutants. We map a classical machine-learning module onto QC, using a neural network constructed from qubit-rotation gates. We have implemented this in simulation and on two commercial quantum computers. We find that the QML models can perform on par with, if not better than, classical baselines. In summary, HypaCADD offers a successful strategy for leveraging QC for CADD.

bioinformatics↗

bioRxiv2021

PLIGHT: A tool to assess privacy risk by inferring identifying characteristics from sparse, noisy genotypes

Single nucleotide polymorphisms (SNPs) from omics data carry a high risk of reidentification for individuals and their relatives. While the ability of thousands of SNPs (especially rare ones) to identify individuals has been repeatedly demonstrated, the ready availability of small sets of noisy genotypes - such as from environmental DNA samples or functional genomics data - motivated us to quantify their informativeness. Here, we present a computational tool suite, PLIGHT ("Privacy Leakage by Inference across Genotypic HMM Trajectories"), that employs population-genetics-based Hidden Markov Models of recombination and mutation to find piecewise alignment of small, noisy query SNP sets to a reference haplotype database. We explore cases where query individuals are either known to be in a database, or not, and consider a variety of queries, including simulated genotype "mosaics" (composites from 2 source individuals) and genotypes from swabs of coffee cups from a known individual. Using PLIGHT on a database with ~5,000 haplotypes, we find for common, noise-free SNPs that only ten are sufficient to identify individuals, ~20 can identify both components in two-individual simulated mosaics, and 20-30 can identify first-order relatives (parents, children, and siblings). Using noisy coffee-cup-derived SNPs, PLIGHT identifies an individual (within the database) using ~30 SNPs. Moreover, even when the individual is not in the database, local genotype matches allow for some phenotypic information leakage based on coarse-grained GWAS SNP imputation and polygenic risk scores. Overall, PLIGHT maximizes the identifying information content of sparse SNP sets through exact or partial matches to databases. Finally, by quantifying such privacy attacks, PLIGHT helps determine the value of selectively sanitizing released SNPs without explicit assumptions about underlying population membership or allele frequencies. To make this practical, we provide a sanitization tool to remove the most identifying SNPs from a query set.

bioinformatics↗

bioRxiv2020

Broad transcriptomic dysregulation across the cerebral cortex in ASD

Classically, psychiatric disorders have been considered to lack defining pathology, but recent work has demonstrated consistent disruption at the molecular level, characterized by transcriptomic and epigenetic alterations.1-3 In ASD, upregulation of microglial, astrocyte, and immune signaling genes, downregulation of specific synaptic genes, and attenuation of regional gene expression differences are observed.1,2,4-6 However, whether these changes are limited to the cortical association areas profiled is unknown. Here, we perform RNA-sequencing (RNA-seq) on 725 brain samples spanning 11 distinct cortical areas in 112 ASD cases and neurotypical controls. We identify substantially more genes and isoforms that differentiate ASD from controls than previously observed. These alterations are pervasive and cortex-wide, but vary in magnitude across regions, roughly showing an anterior to posterior gradient, with the strongest signal in visual cortex, followed by parietal cortex and the temporal lobe. We find a notable enrichment of ASD genetic risk variants among cortex-wide downregulated synaptic plasticity genes and upregulated protein folding gene isoforms. Finally, using snRNA-seq, we determine that regional variation in the magnitude of transcriptomic dysregulation reflects changes in cellular proportion and cell-type-specific gene expression, particularly impacting L3/4 excitatory neurons. These results highlight widespread, genetically-driven neuronal dysfunction as a major component of ASD pathology in the cerebral cortex, extending beyond association cortices to involve primary sensory regions.

neuroscience↗