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Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis
Open accessPublished version of a preprint
Abstract
Spatiotemporal gene expression in ALS: Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease that affects nerve cells in the brain and the spinal cord. It has proven difficult to identify the early stages of disease and where it spreads within the body. Maniatis et al. used RNA sequencing to define transcriptomic changes over the course of disease in different regions of the spinal cord of a mouse ALS model and a postmortem human ALS spinal cord. From changes in gene expression, they identified disease-associated pathways and established the key steps in motor neuron degeneration observed in ALS. Science , this issue p. 89
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“Statistical methods that failed to account for variability between biological replicates identified thousands of DE genes within each region (Fig. 4f and Supplementary Fig. 6e).”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [24]
In the citing paper
“Maniatis et al. provided a comprehensive characterization of transcriptional changes in spinal cords of an ALS mouse model and patients.”
From Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease (2020) · cites this paper as Maniatis et al., 2019
In the citing paper
“the dysregulation of module 8 can be rescued by ablation of autophagy in neurons in Atg7 cKO mice (Maniatis et al., 2019).”
From Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease (2020) · cites this paper as Maniatis et al., 2019
In the citing paper
“The co-expression networks are cell-type-annotated at the level of submodule levels.”
From Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease (2020) · cites this paper as Maniatis et al., 2019
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“Finally, we compared all marker genes that we highlight in this paper (those in Table 1 and all figures) to their patterns in a recent spatial transcriptomics analysis of the spinal cord28 and to the Allen29 and Gensat30 expression databases and found general concordance between these resources (Supplementary Table 4).”
From A harmonized atlas of mouse spinal cord cell types and their spatial organization (2021) · cites this paper as [28]
Citations
Cites
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
Inference from iterative simulation using multiple sequences
Gelman, A., & Rubin, D. B. (1992) · Statistical Science · doi:10.1214/ss/1177011136 · Cited by 16,627
Neurotoxic reactive astrocytes are induced by activated microglia
Liddelow, S. A., Guttenplan, K. A., Clarke, L. E., Bennett, F. C., Bohlen, C. J., Schirmer, L., Bennett, M. L., Münch, A. E., Chung, W.-S., Peterson, T. C., et al. (2017) · Nature · doi:10.1038/nature21029 · Cited by 7,971
Stan : A Probabilistic Programming Language
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017) · Journal of Statistical Software · doi:10.18637/jss.v076.i01 · Cited by 7,754
Regression Analysis of Count Data
Cameron, A. C., & Trivedi, P. K. (2013) · doi:10.1017/cbo9781139013567 · Cited by 6,640
ImageJ2: ImageJ for the next generation of scientific image data
Rueden, C., Schindelin, J., Hiner, M., DeZonia, B. E., Walter, A., Arena, E. T., & Eliceiri, K. W. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1934-z · Cited by 6,087
A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease
Keren-Shaul, H., Spinrad, A., Weiner, A., Matcovitch-Natan, O., Dvir-Szternfeld, R., Ulland, T. K., David, E., Baruch, K., Lara-Astaiso, D., Toth, B., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.018 · Cited by 5,493
Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
Lambert, D. (1992) · Technometrics · doi:10.2307/1269547 · Cited by 3,988
Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834
Self-citationKrasemann, S., Madore, C., Cialic, R., Baufeld, C., Calcagno, N., El Fatimy, R., Beckers, L., O’Loughlin, E., Xu, Y., Fanek, Z., et al. (2017) · Immunity · doi:10.1016/j.immuni.2017.08.008 · Cited by 2,979
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- H. Jeffreys Theory of Probability (Oxford Univ. Press New York ed. 3 1998) Oxford Classic Texts in the Physical Sciences .
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Ismail, S. I., Saad, C., Elrayess, M. A., Khleifat, A. A., Hendi, N. N., Al-Sarraj, Y. A., Badji, R. M., & Al‐Muftah, W. (2026) · Nature Genetics · doi:10.1038/s41588-026-02663-2 · Cited by 5
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Liau, E. S., Jin, S., Chen, Y., Liu, W.-S., Yong, L. W., Tsai, C.-T., Calon, M., Yu, J., Su, Y., Nedelec, S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.29.462340 · Cited by 3
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Zhu, J., Deng, R., Guo, J., Yao, T., Lu, S., Qu, C., Xiong, J., Zhu, Y., Lu, Z., Yang, Y., et al. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag255 · Cited by 5
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Lotti, F., & Przedborski, S. (2022) · Advances in neurobiology · doi:10.1007/978-3-031-07167-6_13 · Cited by 2
Zhong, X., Li, C., Li, Y., Huang, Y., Liu, J., Jiang, A., Chen, J., & Peng, Y. (2024) · Molecular Neurobiology · doi:10.1007/s12035-024-04065-z · Cited by 2
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Pandit, K., Petrescu, J., Cuevas, M., Stephenson, W., Smibert, P., Phatnani, H. P., & Maniatis, S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.27.457864 · Cited by 1
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Gong, L., Gu, Y., Han, X., Luan, C., Wang, X., Sun, Y., Fang, M., Yang, S., Xu, L., Sun, H., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.20.473346 · Cited by 1
Cunha‐Oliveira, T., da Silva, D. F., Segura, L., Baldeiras, I., Marques, R., Rosenstock, T. R., Oliveira, P. J., & Silva, F. S. G. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.03.482309 · Cited by 1
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Cao, S., Sheehan, M. J., Cox, V., Walther, D. M., Guise, A. J., Plowey, E. D., Zavodszky, M. I., Carlile, T. M., Challa, R., Chen, Y., et al. (2025) · SSRN Electronic Journal · doi:10.1101/2025.08.26.672029 · Cited by 1
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Sun, X., Hudson, H. R., Orr, T. C., Koutarapu, S., Rosenbloom, A. B., Ingalls, M. H., Braubach, O., Keene, C. D., Beechem, J., & Orr, M. E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.16.688710 · Cited by 1
杨榕 (2012) · 福建艺术
Zhou, S., Gong, L., Gu, Y., Han, X., Luan, C., Wang, X., Sun, Y., Fang, M., Yang, S., Xu, L., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1210885/v1
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Samee, M. A. H. (2022) · Cell Systems · doi:10.1016/j.cels.2022.04.004
Wu, H., Li, J., Li, X., Gao, M., Tian, W., Chen, D., Zhou, Z., & Liu, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1837739/v1
Wang, Z., Li, Z., Luan, T., Cui, G., Shu, S., Liang, Y., Xiao, J., Zhang, K., Yu, W., Cui, J., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.07.511269
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Okochi, Y., Matsui, T., Sakaguchi, S., Kondo, T., & Naoki, H. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.16.520397
Alqahtani, S. M. (2022) · Journal of Biosciences and Medicines · doi:10.4236/jbm.2022.1012006
Lu, L., Xiao, J., Yu, X., Meng, F., Zhang, Y., Zhou, W., Ren, Y., Li, J., Sun, Y., Sun, H., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-3820516/v1
Ali, L. M., Yim, A. K., Gerbi, E., Nguyen, T., Tu, N., Ikede, F., Sampaleanu, R., Grigore, D., Waligorski, J. E., Kremitzki, C. L., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.02.605366
Lin, L., Jensen, N. M., Ferreira, S. A., Delaidelli, A., Moreira, D. G., Ahlgreen, O. A., Richner, M., Sorensen, P. H. B., Romero‐Ramos, M., Vægter, C. B., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.27.615378
(2024) · Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration · doi:10.1080/21678421.2024.2403299
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(2024) · Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration · doi:10.1080/21678421.2024.2403301
Kandror, E. K., Wang, A., Carrière, M., Peterson, A., Liao, W., Tjärnberg, A., Fung, J. H., Mahbubani, K. T. A., Loper, J., Pangburn, W., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.10.632483
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Çağlar, C., & Gürsoy, B. K. (2025) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00282-7
Belalov, I., Wang, Y., Buzdin, A. A., & Li, X. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-19110-7.00003-3
DNA self-assembly encoding in spatial multi-omics imaging
Tang, X., Diao, Q., Zhu, X., & Pan, Q. (2025) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2025.102811
Teixeira‐Pinheiro, L. C., Gonçalves, R. G. J., Furtado, M., Decotelli, A. B., Vasques, J. F., Maturano, M., Barbosa, R. A. Q., da Costa, F. V. M., Souza, L. R. Q., Lima, M. N., et al. (2025) · Neuroscience · doi:10.1016/j.neuroscience.2025.09.029
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Nuovo, G. J., Crilly, C. J., Lietzke, A. C., & Sawant, D. B. (2025) · CNS & Neurological Disorders - Drug Targets · doi:10.2174/0118715273426855250910082512
Computer Vision Methods for Spatial Transcriptomics: A SurveyPreprint
Zhu, J., Deng, R., Guo, J., Yao, T., Lu, S., Qu, C., Xiong, J., Zhu, Y., Lu, Z., Yang, Y., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.13.682148
Integrative omics data analysis applied in cancer research
Li, Y., Lingwen, D., & Yang, H. H. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-22253-5.00014-5
Metadata
- Authors
- Maniatis, S., Äijö, T., Vickovic, S., Braine, C., Kang, K., Mollbrink, A., Fagegaltier, D., Andrusivová, Ž., Saarenpää, S., Saiz-Castro, G., Cuevas, M., Watters, A., Lundeberg, J., Bonneau, R., & Phatnani, H.
- Affiliations
- New York Genome Center (US)
- Flatiron Institute (US)
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA.
- Science for Life Laboratory (SE)
- Mortimer B. Zuckerman Mind Brain Behavior Institute (US)
- Corresponding author
- Lundeberg, Joakim
- Bonneau, Richard
- Phatnani, Hemali
- ORCID
- Venue
- Science
- Published
- 2019-04-05
- Volume
- 364
- Issue
- 6435
- Pages
- 89-93
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2952459265
- Cited by
- 429
- References
- 55
- Citations by year
- 2026: 38 · 2025: 62 · 2024: 56 · 2023: 58 · 2022: 76 · 2021: 74 · 2020: 50 · 2019: 13
- FWCI
- 36.20
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Simons Foundation
- Target ALS
- Knut och Alice Wallenbergs Stiftelse (2016.0454)
- the ALS Association (15-LGCA-234)
- The Tow Foundation
- Topics
- Health Sciences › Medicine › Clinical Neurology › Amyotrophic Lateral Sclerosis Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- amyotrophic lateral sclerosis, motor neuron degeneration, spinal cord transcriptome, microglia, astrocytes, glial interactions, disease progression, gene expression dynamics, transcriptional pathways
- MeSH terms
- Amyotrophic Lateral Sclerosis, Animals, Astrocytes, Disease Models, Animal, Humans, Motor Neurons, Nerve Degeneration, Neuroglia, Postmortem Changes, Spinal Cord, Gene Expression, Microglia, Muscle, Skeletal, Gene Expression Profiling, Mice, Transcriptome, Spatio-Temporal Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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