We work in different fields of Neurobiology

Integrative functional genomic approaches to understand neurodegenerative diseases

We are using multi-omic approaches to understand molecular mechanism of neurodegenerative diseases like Alzheimer’s disease and Fronto-temporal dementia (FTD). Researchers need a better understanding of the role of distinct cell-types in disease pathophysiology to improve the design of therapeutic interventions for AD and FTD. Current transcriptomic approaches can powerfully investigate quantitative molecular phenotypes and pathways underlying disease progression in a genome-wide manner. Yet, they lack the specificity needed to comprehend the role of cell-type specific changes in disease pathophysiology. Our work will identify the molecular mechanisms underlying neurodegeneration and investigate how they differ from normal aging. Using single-cell multi-omic approaches coupled with bulk tissue sequencing, we hope to directly answer some of these questions.

Unraveling mechanisms involved in neuronal injury and regeneration using systems biology approaches

We are using systems biology approaches to understand the transcriptomic and proteomic changes happening during nerve injury both in the peripheral and central nervous system. While the regeneration capacity of injured neuron in the central nervous system (CNS) is limited, peripheral nervous system (PNS) maintain some capacity to regenerate. Using genomic approaches, we are trying to understand the temporal changes happening in PNS and CNS neurons after injury. Working with Adelson Program in Neural Repair and Rehabilitation (APNRR) researchers, we are also interested in unravel core regulators of neuronal regeneration and find novel drug targets to promote regeneration.


Alim I, Caulfield JT, Chen Y, Swarup V, Geschwind DH, Ivanova E, Seravalli J, Ai Y, Sansing LH, Ste Marie EJ, Hondal RJ, Mukherjee S, Cave JW, Sagdullaev BT, Karuppagounder SS, Ratan RR. Selenium Drives a Transcriptional Adaptive Program to Block Ferroptosis and Treat Stroke. Cell. 2019 May 16;177(5):1262-1279.e25. doi: 10.1016/j.cell.2019.03.032. Epub 2019 May 2.

PsychENCODE Consortium. Revealing the brain's molecular architecture. Science. 2018 Dec 14;362(6420):1262-1263. doi: 10.1126/science.362.6420.1262.

Swarup V, Hinz FI, Rexach JE, Noguchi KI, Toyoshiba H, Oda A, Hirai K, Sarkar A, Seyfried NT, Cheng C, Haggarty SJ; International Frontotemporal Dementia Genomics Consortium, Grossman M, Van Deerlin VM, Trojanowski JQ, Lah JJ, Levey AI, Kondou S, Geschwind DH. Identification of evolutionarily conserved gene networks mediating neurodegenerative dementia. Nat Med. 2019 Jan;25(1):152-164. doi: 10.1038/s41591-018-0223-3. Epub 2018 Dec 3.

Chandran V, Gao K, Swarup V, Versano R, Dong H, Jordan MC, Geschwind DH. Inducible and reversible phenotypes in a novel mouse model of Friedreich's Ataxia. Elife. 2017 Dec 19;6. pii: e30054. doi: 10.7554/eLife.30054.

Parras A, Anta H, Santos-Galindo M, Swarup V, Elorza A, Nieto-González JL, Picó S, Hernández IH, Díaz-Hernández JI, Belloc E, Rodolosse A, Parikshak NN, Peñagarikano O, Fernández-Chacón R, Irimia M, Navarro P, Geschwind DH, Méndez R, Lucas JJ. Autism-like phenotype and risk gene mRNA deadenylation by CPEB4 mis-splicing. Nature. 2018 Aug;560(7719):441-446. doi: 10.1038/s41586-018-0423-5. Epub 2018 Aug 15

Seyfried NT, Dammer EB, Swarup V, Nandakumar D, Duong DM, Yin L, Deng Q, Nguyen T, Hales CM, Wingo T, Glass J, Gearing M, Thambisetty M, Troncoso JC, Geschwind DH, Lah JJ, Levey AI. A Multi-network Approach Identifies Protein-Specific Co-expression in Asymptomatic and Symptomatic Alzheimer's Disease. Cell Syst. 2017 Jan 25;4(1):60-72.e4. doi: 10.1016/j.cels.2016.11.006. Epub 2016 Dec 15.

Parikshak NN, Swarup V, Belgard TG, Irimia M, Ramaswami G, Gandal MJ, Hartl C, Leppa V, Ubieta LT, Huang J, Lowe JK, Blencowe BJ, Horvath S, Geschwind DH. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature. 2016 Dec 15;540(7633):423-427. doi: 10.1038/nature20612. Epub 2016 Dec 5. Erratum in: Nature. 2018 Aug;560(7718):E30.

Swarup V, Geschwind DH. Alzheimer's disease: From big data to mechanism. Nature. 2013 Aug 1;500(7460):34-5. doi: 10.1038/nature12457. Epub 2013 Jul 24.

Swarup V, Audet JN, Phaneuf D, Kriz J, Julien JP. Abnormal regenerative responses and impaired axonal outgrowth after nerve crush in TDP-43 transgenic mouse models of amyotrophic lateral sclerosis. J Neurosci. 2012 Dec 12;32(50):18186-95. doi: 10.1523/JNEUROSCI.2267-12.2012.

Swarup V, Phaneuf D, Dupré N, Petri S, Strong M, Kriz J, Julien JP. Deregulation of TDP-43 in amyotrophic lateral sclerosis triggers nuclear factor κB-mediated pathogenic pathways. J Exp Med. 2011 Nov 21;208(12):2429-47. doi: 10.1084/jem.20111313. Epub 2011 Nov 14.

Swarup V, Phaneuf D, Bareil C, Robertson J, Rouleau GA, Kriz J, Julien JP. Pathological hallmarks of amyotrophic lateral sclerosis/frontotemporal lobar degeneration in transgenic mice produced with TDP-43 genomic fragments. Brain. 2011 Sep;134(Pt 9):2610-26. doi: 10.1093/brain/awr159. Epub 2011 Jul 13.

Enabling research through Big data


Lab Members

Take a closer look into our amazing team.

Neethu Michael

Postdoctoral Scholar

Neethu graduated from University of Göttingen, Germany with a PhD in Neuroscience. Her work at UCI focuses on investigating the regulators of neurodegeneration by integrating human pathological specimens with mouse models of Alzheimer’s disease. Apart from science, she loves cooking.

Emily Miyoshi

INP Graduate Student

Emily received her B.S. in Neuroscience at University of California, Los Angeles. Her work is focused on modeling Alzheimer’s disease with human induced pluripotent stem cells, using an integrative genomics approach to elucidate mechanisms underlying neurodegeneration. She enjoys spending the weekends at the Huntington dog beach with her 50lb labradoodle.

Samuel Morabito

Graduate Student, MCSB Program

Samuel received his B.S. in Bioengineering: Bioinformatics at the University of California, San Diego. His work focuses on identifying and characterizing transcriptional and epigenetic systems in neurodegenration through analysis of large multi-omic datasets. Outside of the lab Samuel enjoys spending time with his cats, at the beach, or playing Mario Kart.


  • NB206 Molecular Neuroscience (Fall Quarter)

    NB206 is a core course of Interdepartmental Neuroscience Graduate Program (INP) at UCI. The course is aimed to understand molecular and cellular mechanisms involved in neuronal function, including control of gene expression, post-transcriptional and post-translational processing, RNA and protein targeting, cell death mechanisms, neurogenetics, and molecular genetic basis of neurological disorders. NB206 is team taught and Dr. Swarup co-teaches with Drs. Blurton-Jones and Cramer. Dr.Swarup teaches the gene regulation and neurogenetics portion of the course highlighting cutting edge genomic approaches that are available to investigate and understand gene-regulation in an unbiased genome-wide manner.

  • Bioinformatics and Systems Biology (Winter Quarter)

    This course is an advanced course geared towards gaining practical hands-on experience in analyzing RNA-seq, ATAC-seq, proteomics and single-cell RNA-seq data. Dr. Swarup will teach the basics of data processing and QC, differential gene/protein expression, co-expression network analysis, and correlation analysis. Since students taking this course are not required to have any programming skills, Dr. Swarup will also teach basics in Linux/Bash command line and statistical programming language R.

  • Bio 37: Brain Dysfunction and Repair (Spring Quarter)

    Bio 37 is team taught and Dr. Swarup teaches with Drs. Green and Busciglio. This course aimed at non-Biological science Majors with an interest in understanding how the brain works and how it controls our bodies and gives rise to our thoughts, emotions and memories, and how it goes awry in disease. Bio 37 requires no prior biology knowledge and will cover how the cells that make up our brains function together. Dr Swarup will teach how the brain is impacted in a number of brain disorders, including Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, Down Syndrome, Depression, Anxiety, Frontotemporal Dementias, and damage due to injury to specific brain regions.


Datasets Analyzed by the Lab

Single-nuclei epigenomic and transcriptomic landscape in Alzheimer's disease

The gene regulatory landscape of the brain is highly dynamic in health and disease, coordinating a menagerie of biological processes across distinct cell-types and cell states. Fully contextualizing molecular signatures of disease with respect to specific cell-types requires a holistic multi-layered experimental and analytical approach. While single-cell transcriptomics has been used extensively in human disease systems, very few single-cell epigenomic studies have been carried out in primary disease samples. Here, we present a multi-omic single-cell study of 191,897 nuclei in late-stage Alzheimer’s Disease (AD), in which we profiled chromatin accessibility and gene expression in the same biological samples, uncovering vast glial heterogeneity in late-stage AD. We describe cis-regulatory relationships in specific cell-types at AD risk loci defined by genome wide association studies (GWAS), demonstrating the utility of this multi-omic single-cell framework for uncovering disease and cell-type-specific regulatory mechanisms. Trajectory analysis of glial populations displayed dynamic transcription factor regulatory patterns in the transition between healthy and diseased states. Further, we introduce scWGCNA, a co-expression network analysis strategy robust to the sparsity of single-cell data, to perform a systems-level meta-analysis of AD transcriptomics. Finally, this work is highly accessible through our intuitive web-portal, allowing for straightforward interrogation of this multi-omic dataset. A shiny app has been generated to easily visualize the data. Visit AD Single-nuclei Multi-Omics Shiny App here

Human Alzheimer's Brain Gene-Expression Datasets

Analysis of Human Alzheimer's disease brain gene-expression datasets generated by AMP-AD consortium as well as other published datasets. All the datasets were processed as described in Morabito et al., 2019 and the consensus network analysis was also performed. A shiny app has been generated to easily visualize the data. Visit AD Gene-Expression Shiny App here

Control Brain Single-Nuclei Expression Dataset

Analysis of Human brain single-nuclei gene-expression dataset generated from 4 control brain samples. The dataset was processed and analyzed as described in Morabito et al., 2019. A shiny app has been generated to easily visualize the data. Visit Control Single-Nuclei Gene-Expression Shiny App here

It's just not about data


3224 Biological Sciences III
Irvine, CA 92697.


(949) 824-3182