Single-cell analysis of innate spinal cord regeneration identifies intersecting modes of neuronal repair
(2024) Nature Communications, 15 (1), art. no. 6808, .
Saraswathy, V.M.a b c , Zhou, L.a b c , Mokalled, M.H.a b c
a Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, United States
b Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, United States
c Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, United States
Abstract
Adult zebrafish have an innate ability to recover from severe spinal cord injury. Here, we report a comprehensive single nuclear RNA sequencing atlas that spans 6 weeks of regeneration. We identify cooperative roles for adult neurogenesis and neuronal plasticity during spinal cord repair. Neurogenesis of glutamatergic and GABAergic neurons restores the excitatory/inhibitory balance after injury. In addition, a transient population of injury-responsive neurons (iNeurons) show elevated plasticity 1 week post-injury. We found iNeurons are injury-surviving neurons that acquire a neuroblast-like gene expression signature after injury. CRISPR/Cas9 mutagenesis showed iNeurons are required for functional recovery and employ vesicular trafficking as an essential mechanism that underlies neuronal plasticity. This study provides a comprehensive resource of the cells and mechanisms that direct spinal cord regeneration and establishes zebrafish as a model of plasticity-driven neural repair. © The Author(s) 2024.
Document Type: Article
Publication Stage: Final
Source: Scopus
Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data
(2024) Journal of Neuroscience Methods, 411, art. no. 110250, .
Zhang, X.a , Landsness, E.C.b , Miao, H.b , Chen, W.g , Tang, M.J.b , Brier, L.M.c , Culver, J.P.c d e f , Lee, J.-M.b c d , Anastasio, M.A.a
a Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
b Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
c Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States
d Department of Biomedical Engineering, Washington University School of Engineering, St. Louis, MO 63130, United States
e Department of Electrical and Systems Engineering, Washington University School of Engineering, St. Louis, MO 63130, United States
f Department of Physics, Washington University School of Arts and Science, St. Louis, MO 63130, United States
g Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
Abstract
Background: Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming, invasive and often suffers from low inter- and intra-rater reliability. Therefore, an automated sleep state classification method that operates on spatiotemporal WFCI data is desired. New method: A hybrid network architecture consisting of a convolutional neural network (CNN) to extract spatial features of image frames and a bidirectional long short-term memory network (BiLSTM) with attention mechanism to identify temporal dependencies among different time points was proposed to classify WFCI data into states of wakefulness, NREM and REM sleep. Results: Sleep states were classified with an accuracy of 84 % and Cohen’s κ of 0.64. Gradient-weighted class activation maps revealed that the frontal region of the cortex carries more importance when classifying WFCI data into NREM sleep while posterior area contributes most to the identification of wakefulness. The attention scores indicated that the proposed network focuses on short- and long-range temporal dependency in a state-specific manner. Comparison with existing method: On a held out, repeated 3-hour WFCI recording, the CNN-BiLSTM achieved a κ of 0.67, comparable to a κ of 0.65 corresponding to the human EEG/EMG-based scoring. Conclusions: The CNN-BiLSTM effectively classifies sleep states from spatiotemporal WFCI data and will enable broader application of WFCI in sleep research. © 2024 The Authors
Author Keywords
Automated sleep state classification; CNN-BiLSTM; Deep learning; Local sleep; Wide-field calcium imaging
Document Type: Article
Publication Stage: Final
Source: Scopus
Early Adversity and Socioeconomic Factors in Pediatric Multiple Sclerosis: A Case-Control Study
(2024) Neurology(R) Neuroimmunology & Neuroinflammation, 11 (5), p. e200282.
Jensen, S.K.G., Camposano, S., Berens, A., Waltz, M., Krupp, L.B., Charvet, L., Belman, A.L., Aaen, G.S., Benson, L.A., Candee, M., Casper, T.C., Chitnis, T., Graves, J., Wheeler, Y.S., Kahn, I., Lotze, T.E., Mar, S.S., Rensel, M., Rodriguez, M., Rose, J.W., Rubin, J.P., Tillema, J.-M., Waldman, A.T., Weinstock-Guttman, B., Barcellos, L.F., Waubant, E., Gorman, M.P., US Network of Pediatric Multiple Sclerosis Centers
From the Pediatric Multiple Sclerosis and Related Disorders Program (S.K.G.J., S.C., L.A.B., M.P.G.), Department of Neurology, Boston Children’s Hospital, Harvard Medical School; Boston College (S.K.G.J.), School of Social Work, MA; Stanford University School of Medicine (A.B.), Palo Alto, CA; Department of Pediatrics (M.W., T.C.C.), University of Utah, Salt Lake City; Pediatric Multiple Sclerosis Center (L.B.K., L.C., A.L.B.), NYU Grossman School of Medicine, New York; Pediatric Multiple Sclerosis Center at Loma Linda University Children’s Hospital (G.S.A.), Loma Linda University, CA; Primary Children’s Hospital (M.C.), University of Utah, Salt Lake City; Division of Child Neurology (T.C.), Department of Neurology, Massachusetts General Hospital, Boston; UCSD Pediatric MS Center (J.G.), San Diego, CA; Center for Pediatric-Onset Demyelinating Disease at the Children’s of Alabama (Y.S.W.), University of Alabama, Birmingham; Children’s National Medical Center (I.K.), Washington, DC; Baylor College of Medicine (T.E.L.), Houston, TX; Washington University Pediatric MS and other Demyelinating Disease Center (S.S.M.), Washington University in St. Louis, MO; Mellen Center for Multiple Sclerosis (M. Rensel), Cleveland Clinic, OH; Mayo Clinic (M. Rodriguez, J.-M.T.), Rochester, MN; Department of Neurology (J.W.R.), University of Utah, Salt Lake City; Ann and Robert H. Lurie Children’s Hospital of Chicago (J.P.R.), IL; Division of Neurology (A.T.W.), Children’s Hospital of Philadelphia, PA; The Pediatric MS Center at the Jacobs Neurological Institute (B.W.-G.), State University of New York at Buffalo; Division of Epidemiology and Genetic Epidemiology and Genomics Laboratory (L.F.B.), School of Public Health, University of California Berkeley; and UCSF Weill Institute for Neurosciences (E.W.), University of California San Francisco
Abstract
BACKGROUND AND OBJECTIVES: Psychosocial adversity and stress, known to predispose adults to neurodegenerative and inflammatory immune disorders, are widespread among children who experience socioeconomic disadvantage, and the associated neurotoxicity and proinflammatory profile may predispose these children to multiple sclerosis (MS). We sought to determine associations of socioeconomic disadvantage and psychosocial adversity with odds of pediatric-onset MS (POMS), age at POMS onset, and POMS disease activity. METHODS: This case-control study used data collected across 17 sites in the United States by the Environmental and Genetic Risk Factors for Pediatric Multiple Sclerosis Study. Cases (n = 381) were youth aged 3-21 years diagnosed with POMS or a clinically isolated demyelinating syndrome indicating high risk of MS. Frequency-matched controls (n = 611) aged 3-21 years were recruited from the same institutions. Prenatal and postnatal adversity and postnatal socioeconomic factors were assessed using retrospective questionnaires and zip code data. The primary outcome was MS diagnosis. Secondary outcomes were age at onset, relapse rate, and Expanded Disability Status Scale (EDSS). Predictors were maternal education, maternal prenatal stress events, child separation from caregivers during infancy and childhood, parental death during childhood, and childhood neighborhood disadvantage. RESULTS: MS cases (64% female, mean age 15.4 years, SD 2.8) were demographically similar to controls (60% female, mean age 14.9 years, SD 3.9). Cases were less likely to have a mother with a bachelor’s degree or higher (OR 0.42, 95% CI 0.22-0.80, p = 0.009) and were more likely to experience childhood neighborhood disadvantage (OR 1.04 for each additional point on the neighborhood socioeconomic disadvantage score, 95% CI 1.00-1.07; p = 0.025). There were no associations of the socioeconomic variables with age at onset, relapse rate, or EDSS, or of prenatal or postnatal adverse events with risk of POMS, age at onset, relapse rate, or EDSS. DISCUSSION: Low socioeconomic status at the neighborhood level may increase the risk of POMS while high parental education may be protective against POMS. Although we did not find associations of other evaluated prenatal or postnatal adversities with POMS, future research should explore such associations further by assessing a broader range of stressful childhood experiences.
Document Type: Article
Publication Stage: Final
Source: Scopus
Mice lacking Astn2 have ASD-like behaviors and altered cerebellar circuit properties
(2024) Proceedings of the National Academy of Sciences of the United States of America, 121 (34), pp. e2405901121.
Hanzel, M.a , Fernando, K.b , Maloney, S.E.c , Horn, Z.a d , Gong, S.e , Mätlik, K.a , Zhao, J.a , Pasolli, H.A.f , Heissel, S.g , Dougherty, J.D.c h , Hull, C.b , Hatten, M.E.a
a Laboratory of Developmental Neurobiology, Rockefeller University, NY, NY 10065, United States
b Neurobiology Department, Duke University, Durham, United Kingdom
c Department of Psychiatry and the Intellectual and Developmental Disabilities Research Center, Washington University Medical School, St. Louis, MO 63130, United States
d InVitro Cell Research LLC, Englewood, United States
e Helen and Robert Appel Alzheimer’s Disease Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, NY, NY 10021, United States
f Electron Microscopy Resource Center, Rockefeller University, NY, NY 10065, United States
g Proteomics Resource Center, Rockefeller University, NY, NY 10065, United States
h Department of Genetics, Washington University Medical School, St. Louis, MO 63130, United States
Abstract
Astrotactin 2 (ASTN2) is a transmembrane neuronal protein highly expressed in the cerebellum that functions in receptor trafficking and modulates cerebellar Purkinje cell (PC) synaptic activity. Individuals with ASTN2 mutations exhibit neurodevelopmental disorders, including autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), learning difficulties, and language delay. To provide a genetic model for the role of the cerebellum in ASD-related behaviors and study the role of ASTN2 in cerebellar circuit function, we generated global and PC-specific conditional Astn2 knockout (KO and cKO, respectively) mouse lines. Astn2 KO mice exhibit strong ASD-related behavioral phenotypes, including a marked decrease in separation-induced pup ultrasonic vocalization calls, hyperactivity, repetitive behaviors, altered behavior in the three-chamber test, and impaired cerebellar-dependent eyeblink conditioning. Hyperactivity and repetitive behaviors are also prominent in Astn2 cKO animals, but they do not show altered behavior in the three-chamber test. By Golgi staining, Astn2 KO PCs have region-specific changes in dendritic spine density and filopodia numbers. Proteomic analysis of Astn2 KO cerebellum reveals a marked upregulation of ASTN2 family member, ASTN1, a neuron-glial adhesion protein. Immunohistochemistry and electron microscopy demonstrate a significant increase in Bergmann glia volume in the molecular layer of Astn2 KO animals. Electrophysiological experiments indicate a reduced frequency of spontaneous excitatory postsynaptic currents (EPSCs), as well as increased amplitudes of both spontaneous EPSCs and inhibitory postsynaptic currents in the Astn2 KO animals, suggesting that pre- and postsynaptic components of synaptic transmission are altered. Thus, ASTN2 regulates ASD-like behaviors and cerebellar circuit properties.
Author Keywords
ASTN2; autism spectrum disorder; cerebellum; neurodevelopmental disorder; Purkinje cell
Document Type: Article
Publication Stage: Final
Source: Scopus
Disruption of the nascent polypeptide-associated complex leads to reduced polyglutamine aggregation and toxicity
(2024) PLoS ONE, 19 (8 AUGUST), art. no. e0303008, .
Dublin-Ryan, L.B., Bhadra, A.K., True, H.L.
Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, MO, United States
Abstract
The nascent polypeptide-associate complex (NAC) is a heterodimeric chaperone complex that binds near the ribosome exit tunnel and is the first point of chaperone contact for newly synthesized proteins. Deletion of the NAC induces embryonic lethality in many multi-cellular organisms. Previous work has shown that the deletion of the NAC rescues cells from prion-induced cytotoxicity. This counterintuitive result led us to hypothesize that NAC disruption would improve viability in cells expressing human misfolding proteins. Here, we show that NAC disruption improves viability in cells expressing expanded polyglutamine and also leads to delayed and reduced aggregation of expanded polyglutamine and changes in polyglutamine aggregate morphology. Moreover, we show that NAC disruption leads to changes in de novo yeast prion induction. These results indicate that the NAC plays a critical role in aggregate organization as a potential therapeutic target in neurodegenerative disorders. © 2024 Dublin-Ryan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Document Type: Article
Publication Stage: Final
Source: Scopus
Brain aging patterns in a large and diverse cohort of 49,482 individuals
(2024) Nature Medicine, .
Yang, Z.a b c , Wen, J.d , Erus, G.a , Govindarajan, S.T.a , Melhem, R.a , Mamourian, E.a , Cui, Y.a , Srinivasan, D.a , Abdulkadir, A.e , Parmpi, P.a , Wittfeld, K.f , Grabe, H.J.f g , Bülow, R.h , Frenzel, S.f , Tosun, D.i , Bilgel, M.j , An, Y.j , Yi, D.k , Marcus, D.S.l , LaMontagne, P.l , Benzinger, T.L.S.l , Heckbert, S.R.m , Austin, T.R.m , Waldstein, S.R.n , Evans, M.K.o , Zonderman, A.B.o , Launer, L.J.p , Sotiras, A.q , Espeland, M.A.r , Masters, C.L.s , Maruff, P.s , Fripp, J.t , Toga, A.W.u , O’Bryant, S.v , Chakravarty, M.M.w , Villeneuve, S.x , Johnson, S.C.y , Morris, J.C.z , Albert, M.S.aa , Yaffe, K.ab , Völzke, H.ac , Ferrucci, L.ad , Nick Bryan, R.ae , Shinohara, R.T.a af , Fan, Y.a , Habes, M.ag , Lalousis, P.A.ah , Koutsouleris, N.ah ai , Wolk, D.A.aj , Resnick, S.M.j , Shou, H.a af , Nasrallah, I.M.a ae , Davatzikos, C.a
a Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
b Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, United States
c GE Healthcare, Bellevue, WA, United States
d Laboratory of AI and Biomedical Science (LABS), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
e Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
f Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
g Site Rostock/Greifswald, German Center for Neurodegenerative Diseases (DZNE), Greifswald, Germany
h Institute of Diagnostic Radiology and Neuroradiology, University of Greifswald, Greifswald, Germany
i Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
j Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
k Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, South Korea
l Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
m Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, United States
n Department of Psychology, University of Maryland, Baltimore County, Baltimore, MD, United States
o Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, United States
p Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, MD, United States
q Department of Radiology and Institute for Informatics, Data Science & amp; Biostatistics, Washington University in St. Louis, St. Louis, MO, United States
r Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States
s Florey Institute, The University of Melbourne, Parkville, VIC, Australia
t CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, QLD, Australia
u Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
v Institute for Translational Research University of North Texas Health Science Center, Fort Worth, TX, United States
w Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Verdun, QC, Canada
x McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
y Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
z Knight Alzheimer Disease Research Center, Dept of Neurology, Washington University School of Medicine, St. Louis, MO, United States
aa Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
ab Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States
ac Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
ad Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, Baltimore, MD, United States
ae Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
af Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & amp; Informatics, University of Pennsylvania, Philadelphia, PA, United States
ag Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
ah Department of Psychosis Studies, Institute of Psychiatry, Psychology & amp; Neuroscience, King’s College London, London, United Kingdom
ai Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
aj Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
Abstract
Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2024.
Document Type: Article
Publication Stage: Article in Press
Source: Scopus