Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis

Bayesian Data Analysis

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) · doi:10.1201/b16018 · Cited by 27,276 · see citing works

Inference from iterative simulation using multiple sequences

Gelman, A., & Rubin, D. B. (1992) · Statistical Science · doi:10.1214/ss/1177011136 · Cited by 16,627 · see citing works

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 · see citing works

<i>Stan</i> : 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 · see citing works

Regression Analysis of Count Data

Cameron, A. C., & Trivedi, P. K. (2013) · doi:10.1017/cbo9781139013567 · Cited by 6,640 · see citing works

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 5,895 · see citing works

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 · see citing works

Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing

Lambert, D. (1992) · Technometrics · doi:10.2307/1269547 · Cited by 3,988 · see citing works

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 · see citing works

The TREM2-APOE Pathway Drives the Transcriptional Phenotype of Dysfunctional Microglia in Neurodegenerative Diseases

Krasemann, 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 · see citing works

Disease-Associated Microglia: A Universal Immune Sensor of Neurodegeneration

Deczkowska, A., Keren-Shaul, H., Weiner, A., Colonna, M., Schwartz, M., & Amit, I. (2018) · Cell · doi:10.1016/j.cell.2018.05.003 · Cited by 1,474 · see citing works

Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding

Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Sample, P., Yao, Z., Graybuck, L. T., Peeler, D. J., Mukherjee, S., Chen, W., et al. (2018) · Science · doi:10.1126/science.aam8999 · Cited by 1,422 · see citing works

Analysis and design of RNA sequencing experiments for identifying isoform regulation

Katz, Y., Wang, E. T., Airoldi, E. M., & Burge, C. B. (2010) · Nature Methods · doi:10.1038/nmeth.1528 · Cited by 1,353 · see citing works

Astrocytes from familial and sporadic ALS patients are toxic to motor neurons

Haidet-Phillips, A. M., Hester, M. E., Miranda, C. J., Meyer, K., Braun, L., Frakes, A., Song, S., Likhite, S., Murtha, M. J., Foust, K. D., et al. (2011) · Nature Biotechnology · doi:10.1038/nbt.1957 · Cited by 782 · see citing works

Bayesian hypothesis testing for psychologists: A tutorial on the Savage–Dickey method

Wagenmakers, E.-J., Lodewyckx, T., Kuriyal, H., & Grasman, R. (2010) · Cognitive Psychology · doi:10.1016/j.cogpsych.2009.12.001 · Cited by 740 · see citing works

Degeneration and impaired regeneration of gray matter oligodendrocytes in amyotrophic lateral sclerosis

Kang, S. H., Li, Y., Fukaya, M., Lorenzini, I., Cleveland, D. W., Ostrow, L. W., Rothstein, J. D., & Bergles, D. E. (2013) · Nature Neuroscience · doi:10.1038/nn.3357 · Cited by 599 · see citing works

Apolipoprotein E Is a Ligand for Triggering Receptor Expressed on Myeloid Cells 2 (TREM2)

Atagi, Y., Liu, C.-C., Painter, M. M., Chen, X.-F., Verbeeck, C., Zheng, H., Li, X., Rademakers, R., Kang, S. S., Xu, H., et al. (2015) · Journal of Biological Chemistry · doi:10.1074/jbc.m115.679043 · Cited by 587 · see citing works

Sall1 is a transcriptional regulator defining microglia identity and function

Buttgereit, A., Lelios, I., Yu, X., Vrohlings, M., Krakoski, N. R., Gautier, E. L., Nishinakamura, R., Becher, B., & Greter, M. (2016) · Nature Immunology · doi:10.1038/ni.3585 · Cited by 570 · see citing works

Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity

Berglund, E., Maaskola, J., Schultz, N., Friedrich, S., Marklund, M., Bergenstråhle, J., Tarish, F., Tanoglidi, A., Vicković, S., Larsson, L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04724-5 · Cited by 550 · see citing works

The Weighted Likelihood Ratio, Linear Hypotheses on Normal Location Parameters

Dickey, J. (1971) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177693507 · Cited by 485 · see citing works

Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways

Miller, J. A., Horvath, S., & Geschwind, D. H. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914257107 · Cited by 480 · see citing works

Neuronal atlas of the dorsal horn defines its architecture and links sensory input to transcriptional cell types

Häring, M., Zeisel, A., Hochgerner, H., Rinwa, P., Jakobsson, J. E. T., Lönnerberg, P., La Manno, G., Sharma, N., Borgius, L., Kiehn, O., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0141-1 · Cited by 470 · see citing works

Proper multivariate conditional autoregressive models for spatial data analysis

Gelfand, A. E. (2003) · Biostatistics · doi:10.1093/biostatistics/4.1.11 · Cited by 458 · see citing works

Immune reactivity in a mouse model of familial ALS correlates with disease progression

Alexianu, M. E., Kozovska, M., & Appel, S. H. (2001) · Neurology · doi:10.1212/wnl.57.7.1282 · Cited by 438 · see citing works

Astrocyte-encoded positional cues maintain sensorimotor circuit integrity

Molofsky, A. V., Kelley, K. W., Tsai, H., Redmond, S., Chang, S., Madireddy, L., Chan, J. R., Baranzini, S. E., Ullian, E. M., & Rowitch, D. H. (2014) · Nature · doi:10.1038/nature13161 · Cited by 316 · see citing works

Astrocytes as a Source for Extracellular Matrix Molecules and Cytokines

Wiese, S., Karus, M., & Faissner, A. (2012) · Frontiers in Pharmacology · doi:10.3389/fphar.2012.00120 · Cited by 262 · see citing works

Molecular and Functional Properties of Regional Astrocytes in the Adult Brain

Morel, L., Chiang, M. S. R., Higashimori, H., Shoneye, T., Iyer, L. K., Yelick, J., Tai, A., & Yang, Y. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3956-16.2017 · Cited by 243 · see citing works

Generalized Hierarchical Multivariate CAR Models for Areal Data

Jin, X., Carlin, B. P., & Banerjee, S. (2005) · Biometrics · doi:10.1111/j.1541-0420.2005.00359.x · Cited by 235 · see citing works

Astrocyte-Derived TGF-β1 Accelerates Disease Progression in ALS Mice by Interfering with the Neuroprotective Functions of Microglia and T Cells

Endo, F., Komine, O., Fujimori-Tonou, N., Katsuno, M., Jin, S., Watanabe, S., Sobue, G., Dezawa, M., Wyss-Coray, T., & Yamanaka, K. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.03.053 · Cited by 207 · see citing works

Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections

Salmén, F., Ståhl, P. L., Mollbrink, A., Navarro, J. F., Vicković, S., Frisén, J., & Lundeberg, J. (2018) · Nature Protocols · doi:10.1038/s41596-018-0045-2 · Cited by 193 · see citing works

Distinct roles for motor neuron autophagy early and late in the SOD1 ^G93A mouse model of ALS

Rudnick, N. D., Griffey, C. J., Guarnieri, P., Gerbino, V., Wang, X., Piersaint, J. A., Tapia, J. C., Rich, M. M., & Maniatis, T. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1704294114 · Cited by 189 · see citing works

Autophagy and mitophagy in ALS

Evans, C. S., & Holzbaur, E. L. F. (2018) · Neurobiology of Disease · doi:10.1016/j.nbd.2018.07.005 · Cited by 178 · see citing works

Characterization of early pathogenesis in the SOD 1G93A mouse model of ALS : part II, results and discussion

Vinsant, S. L., Mansfield, C., Jiménez-Moreno, R., Moore, V. D. G., Yoshikawa, M., Hampton, T., Prevette, D. M., Caress, J. B., Oppenheim, R. W., & Milligan, C. (2013) · Brain and Behavior · doi:10.1002/brb3.142 · Cited by 175 · see citing works

To Matrix, Network, or Hierarchy: That Is the Question

Novick, L. R., & Hurley, S. M. (2001) · Cognitive Psychology · doi:10.1006/cogp.2000.0746 · Cited by 157 · see citing works

Intricate interplay between astrocytes and motor neurons in ALS

Phatnani, H. P., Guarnieri, P., Friedman, B. A., Carrasco, M. A., Muratet, M. A., O’Keeffe, S., Nwakeze, C. L., Pauli, F., Newberry, K. M., Meadows, S. K., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1222361110 · Cited by 146 · see citing works

Glycosphingolipids are modulators of disease pathogenesis in amyotrophic lateral sclerosis

Dodge, J. C., Treleaven, C. M., Pacheco, J., Cooper, S., Bao, C., Abraham, M., Cromwell, M. A., Sardi, S. P., Chuang, W., Sidman, R. L., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508767112 · Cited by 134 · see citing works

Reactive Astrocytes Promote ALS-like Degeneration and Intracellular Protein Aggregation in Human Motor Neurons by Disrupting Autophagy through TGF-β1

Tripathi, P., Rodriguez-Muela, N., Klim, J. R., de Boer, A. S., Agrawal, S., Sandoe, J., Lopes, C. S., Ogliari, K. S., Williams, L. A., Shear, M., et al. (2017) · Stem Cell Reports · doi:10.1016/j.stemcr.2017.06.008 · Cited by 123 · see citing works

Characterization of early pathogenesis in the SOD1 G93A mouse model of ALS : part I, background and methods

Vinsant, S. L., Mansfield, C., Jiménez-Moreno, R., Moore, V. D. G., Yoshikawa, M., Hampton, T., Prevette, D. M., Caress, J. B., Oppenheim, R. W., & Milligan, C. (2013) · Brain and Behavior · doi:10.1002/brb3.143 · Cited by 107 · see citing works

Distribution of the high-affinity choline transporter in the central nervous system of the rat

Misawa, H., Nakata, K., Matsuura, J., Nagao, M., Okuda, T., & Haga, T. (2001) · Neuroscience · doi:10.1016/s0306-4522(01)00147-6 · Cited by 105 · see citing works

Focality, stochasticity and neuroanatomic propagation in ALS pathogenesis

Ravits, J. (2014) · Experimental Neurology · doi:10.1016/j.expneurol.2014.07.021 · Cited by 104 · see citing works

ST Pipeline: an automated pipeline for spatial mapping of unique transcripts

Navarro, J. F., Sjöstrand, J., Salmén, F., Lundeberg, J., & Ståhl, P. L. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx211 · Cited by 103 · see citing works

Amyotrophic lateral sclerosis and denervation alter sphingolipids and up-regulate glucosylceramide synthase

Henriques, A., Croixmarie, V., Priestman, D. A., Rosenbohm, A., Dirrig‐Grosch, S., D’Ambra, E., Huebecker, M., Hussain, G., Boursier-Neyret, C., Echaniz‐Laguna, A., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv439 · Cited by 97 · see citing works

Lipoprotein Lipase Is a Feature of Alternatively-Activated Microglia and May Facilitate Lipid Uptake in the CNS During Demyelination

Bruce, K. D., Gorkhali, S., Given, K., Coates, A. M., Boyle, K. E., Macklin, W. B., & Eckel, R. H. (2018) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2018.00057 · Cited by 94 · see citing works

Inhibition of Lysosome Membrane Recycling Causes Accumulation of Gangliosides that Contribute to Neurodegeneration

Boutry, M., Branchu, J., Lustremant, C., Pujol, C., Pernelle, J., Matusiak, R., Seyer, A., Poirel, M., Chu‐Van, E., Pierga, A., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.05.098 · Cited by 80 · see citing works

Neuromuscular Junction Impairment in Amyotrophic Lateral Sclerosis: Reassessing the Role of Acetylcholinesterase

Campanari, M.-L., García-Ayllón, M.-S., Ciura, S., Sáez-Valero, J., & Kabashi, E. (2016) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2016.00160 · Cited by 69 · see citing works

The astrocytic transporter SLC7A10 (Asc-1) mediates glycinergic inhibition of spinal cord motor neurons

Ehmsen, J. T., Liu, Y. U., Wang, Y., Paladugu, N., Johnson, A. E., Rothstein, J. D., du Lac, S., Mattson, M. P., & Höke, A. (2016) · Scientific Reports · doi:10.1038/srep35592 · Cited by 51 · see citing works

Massive and parallel expression profiling using microarrayed single-cell sequencing

Vicković, S., Ståhl, P. L., Salmén, F., Giatrellis, S., Westholm, J. O., Mollbrink, A., Navarro, J. F., Custodio, J., Bienko, M., Sutton, L., et al. (2016) · Nature Communications · doi:10.1038/ncomms13182 · Cited by 46 · see citing works

A natural human IgM that binds to gangliosides is therapeutic in murine models of amyotrophic lateral sclerosis

Xu, X., Đenić, A., Jordan, L. R., Wittenberg, N. J., Warrington, A. E., Wootla, B., Papke, L. M., Zoecklein, L. J., Yoo, D., Shaver, J., et al. (2015) · Disease Models & Mechanisms · doi:10.1242/dmm.020727 · Cited by 38 · see citing works

Stratified gene expression analysis identifies major amyotrophic lateral sclerosis genes

Jones, A., Troakes, C., King, A., Sahni, V., de Jong, S., Bossers, K., Papouli, E., Mirza, M. M., Al‐Sarraj, S., Shaw, C. E., et al. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.02.017 · Cited by 36 · see citing works

Astrocyte-derived Jagged-1 mitigates deleterious Notch signaling in amyotrophic lateral sclerosis

Nonneman, A., Criem, N., Lewandowski, S. A., Nuyts, R., Thal, D. R., Pfrieger, F. W., Ravits, J., Damme, P. V., Zwijsen, A., Bosch, L. V. D., et al. (2018) · Neurobiology of Disease · doi:10.1016/j.nbd.2018.07.012 · Cited by 31 · see citing works

Personalized Medicine and Molecular Interaction Networks in Amyotrophic Lateral Sclerosis (ALS): Current Knowledge

Morgan, S., Duguez, S., & Duddy, W. J. (2018) · Journal of Personalized Medicine · doi:10.3390/jpm8040044 · Cited by 15 · see citing works

A probabilistic generative model for quantification of DNA modifications enables analysis of demethylation pathways

Äijö, T., Huang, Y., Mannerström, H., Chavez, L., Tsagaratou, A., Rao, A., & Lähdesmäki, H. (2016) · Genome Biology · doi:10.1186/s13059-016-0911-6 · Cited by 14 · see citing works

Spatial models to account for variation in observer effort in bird atlases

Wilson, A., Brauning, D. W., Carey, C. S., & Mulvihill, R. S. (2017) · Ecology and Evolution · doi:10.1002/ece3.3201 · Cited by 4 · see citing works

Report of the Co‐Editors – 2004

Cullis, B. R., Freedman, L. S., Kenward, M., & Lin, X. (2005) · Biometrics · doi:10.1111/j.0006-341x.2005.20050217-1.x · Cited by 1 · see citing works

tare/Splotch: Publication release

Äijö, T. (2019) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.2566611 · Cited by 1 · see citing works

RNA sequencing: the teenage years

Stark, R., Grzelak, M., & Hadfield, J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0150-2 · Cited by 1,906 · see citing works

Exploring tissue architecture using spatial transcriptomics

Rao, A., Barkley, D., França, G. S., & Yanai, I. (2021) · Nature · doi:10.1038/s41586-021-03634-9 · Cited by 1,700 · see citing works

Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

Maynard, K. R., Collado-Torres, L., Weber, L. M., Uytingco, C., Barry, B. K., Williams, S. R., Catallini, J. L., Tran, M. N., Besich, Z., Tippani, M., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00787-0 · Cited by 1,222 · see citing works

Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas

Moncada, R., Barkley, D., Wagner, F., Chiodin, M., Devlin, J. C., Baron, M., Hajdu, C. H., Simeone, D. M., & Yanai, I. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0392-8 · Cited by 1,045 · see citing works

Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00370-8 · Cited by 1,059 · see citing works

Confronting false discoveries in single-cell differential expression

Squair, J. W., Gautier, M., Kathe, C., Anderson, M. A., James, N. D., Hutson, T. H., Hudelle, R., Qaiser, T., Matson, K. J. E., Barraud, Q., et al. (2021) · doi:10.1101/2021.03.12.435024 · Cited by 1,017 · see citing works

Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease

Chen, W.-T., Lu, A., Craessaerts, K., Pavie, B., Sala Frigerio, C., Corthout, N., Qian, X., Laláková, J., Kühnemund, M., Voytyuk, I., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.038 · Cited by 949 · see citing works

A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart

Asp, M., Giacomello, S., Larsson, L., Wu, C., Fürth, D., Qian, X., Wärdell, E., Custodio, J., Reimegård, J., Salmén, F., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.025 · Cited by 740 · see citing works

SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes

Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I., & Heyn, H. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab043 · Cited by 732 · see citing works

Harnessing multimodal data integration to advance precision oncology

Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J., & Shah, S. P. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-021-00408-3 · Cited by 662 · see citing works

Spatially resolved transcriptomes—next generation tools for tissue exploration

Asp, M., Bergenstråhle, J., & Lundeberg, J. (2020) · BioEssays · doi:10.1002/bies.201900221 · Cited by 666 · see citing works

Microglia in Neuroinflammation and Neurodegeneration: From Understanding to Therapy

Muzio, L., Viotti, A., & Martino, G. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.742065 · Cited by 581 · see citing works

Applications of multi‐omics analysis in human diseases

Chen, C., Wang, J., Pan, D., Wang, X., Xu, Y., Yan, J., Wang, L., Yang, X., Yang, M., & Liu, G. (2023) · MedComm · doi:10.1002/mco2.315 · Cited by 545 · see citing works

Single-cell roadmap of human gonadal development

García‐Alonso, L., Lorenzi, V., Icoresi-Mazzeo, C., Alves-Lopes, J. P., Roberts, K., Sancho‐Serra, C., Engelbert, J., Marečková, M., Gruhn, W. H., Botting, R. A., et al. (2022) · Nature · doi:10.1038/s41586-022-04918-4 · Cited by 469 · see citing works

The role of TDP-43 mislocalization in amyotrophic lateral sclerosis

Suk, T. R., & Rousseaux, M. W. C. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00397-1 · Cited by 456 · see citing works

Astrocyte contribution to dysfunction, risk and progression in neurodegenerative disorders

Brandebura, A. N., Paumier, A., Onur, T. S., & Allen, N. J. (2023) · Nature Reviews Neuroscience · doi:10.1038/s41583-022-00641-1 · Cited by 427 · see citing works

Integrative spatial analysis reveals a multi-layered organization of glioblastoma

Greenwald, A., Darnell, N. G., Hoefflin, R., Simkin, D., Mount, C., Castro, L. N. G., Harnik, Y., Dumont, S. D., Hirsch, D., Nomura, M., et al. (2024) · Cell · doi:10.1016/j.cell.2024.03.029 · Cited by 397 · see citing works

Acetylcholinesterase Inhibitors in the Treatment of Neurodegenerative Diseases and the Role of Acetylcholinesterase in their Pathogenesis

Walczak, Ł. J., & Herbet, M. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22179290 · Cited by 351 · see citing works

Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans

Thrupp, N., Sala Frigerio, C., Wolfs, L., Skene, N. G., Fattorelli, N., Poovathingal, S., Fourne, Y., Matthews, P. M., Theys, T., Mancuso, R., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108189 · Cited by 329 · see citing works

Spatially resolved transcriptomics adds a new dimension to genomics

Larsson, L., Frisén, J., & Lundeberg, J. (2021) · Nature Methods · doi:10.1038/s41592-020-01038-7 · Cited by 313 · see citing works

Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease

Piwecka, M., Rajewsky, N., & Rybak-Wolf, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00809-y · Cited by 320 · see citing works

The neurons that restore walking after paralysis

Kathe, C., Skinnider, M. A., Hutson, T. H., Regazzi, N., Gautier, M., Demesmaeker, R., Komi, S., Ceto, S., James, N. D., Cho, N., et al. (2022) · Nature · doi:10.1038/s41586-022-05385-7 · Cited by 304 · see citing works

Alignment and integration of spatial transcriptomics data

Zeira, R., Land, M., Strzalkowski, A., & Raphael, B. J. (2022) · Nature Methods · doi:10.1038/s41592-022-01459-6 · Cited by 298 · see citing works

Seamless integration of image and molecular analysis for spatial transcriptomics workflows

Bergenstråhle, J., Larsson, L., & Lundeberg, J. (2020) · BMC Genomics · doi:10.1186/s12864-020-06832-3 · Cited by 263 · see citing works

A harmonized atlas of mouse spinal cord cell types and their spatial organization

Russ, D. E., Cross, R. B. P., Li, L., Koch, S. C., Matson, K. J. E., Yadav, A., Alkaslasi, M. R., Lee, D. I., Le Pichon, C. E., Menon, V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25125-1 · Cited by 255 · see citing works

SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies

Zhu, J., Sun, S., & Zhou, X. (2021) · Genome biology · doi:10.1186/s13059-021-02404-0 · Cited by 248 · see citing works

Amyotrophic lateral sclerosis: translating genetic discoveries into therapies

Akçimen, F., Lopez, E. R., Landers, J. E., Nath, A., Chiò, A., Chia, R., & Traynor, B. J. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00592-y · Cited by 234 · see citing works

Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex

Chen, A., Sun, Y., Lei, Y., Li, C., Liao, S., Meng, J., Bai, Y., Liu, Z., Liang, Z., Zhu, Z., et al. (2023) · Cell · doi:10.1016/j.cell.2023.06.009 · Cited by 232 · see citing works

Single-cell transcriptomic analysis of the adult mouse spinal cord reveals molecular diversity of autonomic and skeletal motor neurons

Blum, J. A., Klemm, S., Shadrach, J. L., Guttenplan, K. A., Nakayama, L., Kathiria, A., Hoang, P. T., Gautier, O., Kaltschmidt, J. A., Greenleaf, W. J., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00795-0 · Cited by 210 · see citing works

DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence

Song, Q., & Su, J. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa414 · Cited by 224 · see citing works

A cellular taxonomy of the adult human spinal cord

Yadav, A., Matson, K. J. E., Li, L., Hua, I., Petrescu, J., Kang, K., Alkaslasi, M. R., Lee, D. I., Hasan, S., Galuta, A., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.01.007 · Cited by 217 · see citing works

Clinical and translational values of spatial transcriptomics

Zhang, L., Chen, D., Song, D., Liu, X., Zhang, Y., Xu, X., & Wang, X. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-00960-w · Cited by 212 · see citing works

Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks

Zeng, Y., Wei, Z., Yu, W., Yin, R., Yuan, Y., Li, B., Tang, Z., Lu, Y., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac297 · Cited by 211 · see citing works

Spatial transcriptomics: Technologies, applications and experimental considerations

Wang, Y., Liu, B., Zhao, G., Lee, Y., Buzdin, A., Mu, X., Zhao, J., Chen, H., & Li, X. (2023) · Genomics · doi:10.1016/j.ygeno.2023.110671 · Cited by 218 · see citing works

SM-Omics is an automated platform for high-throughput spatial multi-omics

Vicković, S., Lötstedt, B., Klughammer, J., Mages, S., Segerstolpe, Å., Rozenblatt–Rosen, O., & Regev, A. (2022) · Nature Communications · doi:10.1038/s41467-022-28445-y · Cited by 194 · see citing works

Temporal modelling using single-cell transcriptomics

Ding, J., Sharon, N., & Bar-Joseph, Z. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00444-7 · Cited by 195 · see citing works

Spatially resolved transcriptomics: a comprehensive review of their technological advances, applications, and challenges

Cheng, M., Jiang, Y., Xu, J., Mentis, A. A., Wang, S., Zheng, H., Sahu, S. K., Liu, L., & Xu, X. (2023) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2023.03.011 · Cited by 190 · see citing works

Advances in spatial transcriptomics and related data analysis strategies

Du, J., Yang, Y., An, Z.-J., Zhang, M., Fu, X.-H., Huang, Z., Yuan, Y., & Hou, J. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04150-2 · Cited by 189 · see citing works

Integration of spatial and single-cell transcriptomics localizes epithelial cell–immune cross-talk in kidney injury

Ferreira, R. M., Sabo, A. R., Winfree, S., Collins, K. S., Janosevic, D., Gulbronson, C. J., Cheng, Y., Casbon, L., Barwinska, D., Ferkowicz, M. J., et al. (2021) · JCI Insight · doi:10.1172/jci.insight.147703 · Cited by 183 · see citing works

The microglial component of amyotrophic lateral sclerosis

Clarke, B. E., & Patani, R. (2020) · Brain · doi:10.1093/brain/awaa309 · Cited by 164 · see citing works

Distinct oligodendrocyte populations have spatial preference and different responses to spinal cord injury

Floriddia, E., Lourenço, T., Zhang, S., van Bruggen, D., Hilscher, M., Kukanja, P., dos Santos, J. P. G., Altınkök, M., Yokota, C., Llorens-Bobadilla, E., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19453-x · Cited by 137 · see citing works

TREM2 interacts with TDP-43 and mediates microglial neuroprotection against TDP-43-related neurodegeneration

Xie, M., Liu, Y. U., Zhao, S., Zhang, L., Bosco, D. B., Pang, Y.-P., Zhong, J., Sheth, U., Martens, Y. A., Zhao, N., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-00975-6 · Cited by 157 · see citing works

LifeTime and improving European healthcare through cell-based interceptive medicine

Rajewsky, N., Almouzni, G., Gorski, S. A., Aerts, S., Amit, I., Bertero, M. G., Bock, C., Bredenoord, A. L., Cavalli, G., Chiocca, S., et al. (2020) · Nature · doi:10.1038/s41586-020-2715-9 · Cited by 156 · see citing works

Spatial landscapes of cancers: insights and opportunities

Chen, J., Larsson, L., Swarbrick, A., & Lundeberg, J. (2024) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-024-00926-7 · Cited by 153 · see citing works

Integrative transcriptomic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes

Humphrey, J., Venkatesh, S., Hasan, R., Herb, J. T., de Paiva Lopes, K., Küçükali, F., Byrska-Bishop, M., Evani, U. S., Narzisi, G., Fagegaltier, D., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01205-3 · Cited by 138 · see citing works

Spatial host–microbiome sequencing reveals niches in the mouse gut

Lötstedt, B., Stražar, M., Xavier, R. J., Regev, A., & Vicković, S. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01988-1 · Cited by 135 · see citing works

Genome-wide spatial expression profiling in formalin-fixed tissues

Gracia Villacampa, E., Larsson, L., Mirzazadeh, R., Kvastad, L., Andersson, A., Mollbrink, A., Kokaraki, G., Monteil, V., Schultz, N., Appelberg, K. S., et al. (2021) · Cell Genomics · doi:10.1016/j.xgen.2021.100065 · Cited by 132 · see citing works

Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors

Pang, M., Su, K., & Li, M. (2021) · doi:10.1101/2021.11.28.470212 · Cited by 130 · see citing works

Non-cell-autonomous pathogenic mechanisms in amyotrophic lateral sclerosis

Harten, A. C. M. V., Phatnani, H. P., & Przedborski, S. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2021.04.008 · Cited by 126 · see citing works

Cell segmentation-free inference of cell types from in situ transcriptomics data

Park, J., Choi, W., Tiesmeyer, S., Long, B., Borm, L. E., Garren, E. J., Nguyen, T. N., Tasic, B., Codeluppi, S., Graf, T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23807-4 · Cited by 116 · see citing works

Hypoxia and the phenomenon of immune exclusion

Pietrobon, V., & Marincola, F. M. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-020-02667-4 · Cited by 119 · see citing works

Genome-wide identification of the genetic basis of amyotrophic lateral sclerosis

Zhang, S., Cooper‐Knock, J., Weimer, A. K. K., Shi, M., Moll, T., Marshall, J. N. G., Harvey, C., Nezhad, H. G., Franklin, J., Souza, C. D. S., et al. (2022) · Neuron · doi:10.1016/j.neuron.2021.12.019 · Cited by 115 · see citing works

SODB facilitates comprehensive exploration of spatial omics data

Yuan, Z., Pan, W., Zhao, X., Zhao, F., Xu, Z., Li, X., Zhao, Y. C., Zhang, M. Q., & Yao, J. (2023) · Nature Methods · doi:10.1038/s41592-023-01773-7 · Cited by 112 · see citing works

Placental serotonin signaling, pregnancy outcomes, and regulation of fetal brain development†

Rosenfeld, C. S. (2019) · Biology of Reproduction · doi:10.1093/biolre/ioz204 · Cited by 114 · see citing works

The Loss of TBK1 Kinase Activity in Motor Neurons or in All Cell Types Differentially Impacts ALS Disease Progression in SOD1 Mice

Gerbino, V., Kaunga, E., Ye, J., Canzio, D., O’Keeffe, S., Rudnick, N. D., Guarnieri, P., Lutz, C. M., & Maniatis, T. (2020) · Neuron · doi:10.1016/j.neuron.2020.03.005 · Cited by 102 · see citing works

Metabolomics and complementary techniques to investigate the plant phytochemical cosmos

Tsugawa, H., Rai, A., Saito, K., & Nakabayashi, R. (2021) · Natural Product Reports · doi:10.1039/d1np00014d · Cited by 116 · see citing works

Multi-Omics Approaches to Define Calcific Aortic Valve Disease Pathogenesis

Blaser, M. C., Kraler, S., Lüscher, T. F., & Aikawa, E. (2021) · Circulation Research · doi:10.1161/circresaha.120.317979 · Cited by 109 · see citing works

Addressing heterogeneity in amyotrophic lateral sclerosis CLINICAL TRIALS

Goyal, N., Berry, J. D., Windebank, A. J., Staff, N. P., Maragakis, N. J., van den Berg, L. H., Genge, A. L., Miller, R. G., Baloh, R. H., Kern, R. Z., et al. (2020) · Muscle & Nerve · doi:10.1002/mus.26801 · Cited by 110 · see citing works

Spatial Transcriptomics Reveals Genes Associated with Dysregulated Mitochondrial Functions and Stress Signaling in Alzheimer Disease

Navarro, J. F., Croteau, D. L., Jurek, A., Andrusivová, Ž., Yang, B., Wang, Y., Ogedegbe, B., Riaz, T., Støen, M., Desler, C., et al. (2020) · iScience · doi:10.1016/j.isci.2020.101556 · Cited by 107 · see citing works

Opportunities for Artificial Intelligence in Advancing Precision Medicine

Filipp, F. V. (2019) · Current Genetic Medicine Reports · doi:10.1007/s40142-019-00177-4 · Cited by 107 · see citing works

Understanding tumour cell heterogeneity and its implication for immunotherapy in liver cancer using single-cell analysis

Heinrich, S., Craig, A. J., Ma, L., Heinrich, B., Greten, T. F., & Wang, X. W. (2020) · Journal of Hepatology · doi:10.1016/j.jhep.2020.11.036 · Cited by 104 · see citing works

Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation

Monjo, T., Koido, M., Nagasawa, S., Suzuki, Y., & Kamatani, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-07685-4 · Cited by 93 · see citing works

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer’s disease: review, recommendation, implementation and application

Wang, M., Song, W., Chen, M., Wang, Q., Zhou, X., Xu, P., Krek, A., Yoon, Y., Ho, L., Orr, M. E., et al. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00517-z · Cited by 100 · see citing works

Microglia heterogeneity, modeling and cell-state annotation in development and neurodegeneration

Fumagalli, L., Nazlie Mohebiany, A., Premereur, J., Polanco Miquel, P., Bijnens, B., Van de Walle, P., Fattorelli, N., & Mancuso, R. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01931-4 · Cited by 90 · see citing works

Joint cell segmentation and cell type annotation for spatial transcriptomics

Littman, R., Hemminger, Z., Foreman, R., Arneson, D. V., Zhang, G., Gómez‐Pinilla, F., Yang, X., & Wollman, R. (2021) · Molecular Systems Biology · doi:10.15252/msb.202010108 · Cited by 96 · see citing works

Spatiotemporal Dynamics of the Molecular Expression Pattern and Intercellular Interactions in the Glial Scar Response to Spinal Cord Injury

Gong, L., Gu, Y., Han, X., Luan, C., Liu, C., Wang, X., Sun, Y., Zheng, M., Fang, M., Yang, S., et al. (2022) · Neuroscience Bulletin · doi:10.1007/s12264-022-00897-8 · Cited by 93 · see citing works

Principles and challenges of modeling temporal and spatial omics data

Velten, B., & Stegle, O. (2023) · Nature Methods · doi:10.1038/s41592-023-01992-y · Cited by 90 · see citing works

White matter microglia heterogeneity in the CNS

Amor, S., McNamara, N. B., Gerrits, E., Marzin, M. C., Kooistra, S. M., Miron, V. E., & Nutma, E. (2022) · Acta Neuropathologica · doi:10.1007/s00401-021-02389-x · Cited by 89 · see citing works

Single-cell RNA-seq analysis of the brainstem of mutant SOD1 mice reveals perturbed cell types and pathways of amyotrophic lateral sclerosis

Liu, W., Venugopal, S., Majid, S., Ahn, I. S., Diamante, G., Hong, J. S., Yang, X., & Chandler, S. H. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.104877 · Cited by 86 · see citing works

Mapping human cell phenotypes to genotypes with single-cell genomics

Camp, J. G., Platt, R. J., & Treutlein, B. (2019) · Science · doi:10.1126/science.aax6648 · Cited by 83 · see citing works

Statistical and machine learning methods for spatially resolved transcriptomics with histology

Hu, J., Schroeder, A. R., Coleman, K., Chen, C., Auerbach, B. J., & Li, M. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.06.052 · Cited by 81 · see citing works

Spatially resolved transcriptomics and its applications in cancer

Maniatis, S., Petrescu, J., & Phatnani, H. P. (2021) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2020.12.002 · Cited by 80 · see citing works

CROST: a comprehensive repository of spatial transcriptomics

Wang, G., Wu, S., Xiong, Z., Qu, H., Fang, X., & Bào, Y. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad782 · Cited by 84 · see citing works

Oligodendrocyte Dysfunction in Amyotrophic Lateral Sclerosis: Mechanisms and Therapeutic Perspectives

Raffaele, S., Boccazzi, M., & Fumagalli, M. (2021) · Cells · doi:10.3390/cells10030565 · Cited by 86 · see citing works

Spatial maps of hepatocellular carcinoma transcriptomes reveal spatial expression patterns in tumor immune microenvironment

Wang, Y., Yuan, S., Jiang, H., Li, Z., Yin, H.-Z., Tan, J. W., Dai, Z., Ge, C.-M., Sun, S., & Yang, F. (2022) · Theranostics · doi:10.7150/thno.71873 · Cited by 85 · see citing works

dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues

Maynard, K. R., Tippani, M., Takahashi, Y., Phan, B. N., Hyde, T. M., Jaffe, A. E., & Martinowich, K. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa312 · Cited by 83 · see citing works

Three-dimensional spatial transcriptomics uncovers cell type localizations in the human rheumatoid arthritis synovium

Vicković, S., Schapiro, D., Carlberg, K., Lötstedt, B., Larsson, L., Hildebrandt, F., Korotkova, M., Hensvold, A. H., Catrina, A. I., Sorger, P. K., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03050-3 · Cited by 84 · see citing works

Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery

Cao, J., Li, C., Cui, Z., Deng, S., Tong, L., Liu, W., Yang, H., & Chen, P. (2024) · Theranostics · doi:10.7150/thno.95908 · Cited by 89 · see citing works

Single-cell omics: experimental workflow, data analyses and applications

Sun, F., Li, H., Sun, D., Fu, S., Gu, L., Shao, X., Wang, Q., Dong, X., Duan, B., Xing, F., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2561-0 · Cited by 81 · see citing works

Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities

Wang, Y., Ma, S., & Ruzzo, W. L. (2020) · Scientific Reports · doi:10.1038/s41598-020-60384-w · Cited by 79 · see citing works

Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation

Li, J., Jaiswal, M. K., Chien, J.-F., Kozlenkov, A., Jung, J., Zhou, P., Gardashli, M., Pregent, L., Engelberg-Cook, E., Dickson, D. W., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41033-y · Cited by 78 · see citing works

SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells

Tan, X., Su, A., Tran, M., & Nguyen, Q. H. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz914 · Cited by 76 · see citing works

Analysis and Visualization of Spatial Transcriptomic Data

Liu, B., Li, Y., & Zhang, L. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2021.785290 · Cited by 71 · see citing works

The Key Role of Astrocytes in Amyotrophic Lateral Sclerosis and Their Commitment to Glutamate Excitotoxicity

Provenzano, F., Torazza, C., Bonifacino, T., Bonanno, G., & Milanese, M. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242015430 · Cited by 74 · see citing works

Circulating RNAs as Potential Biomarkers in Amyotrophic Lateral Sclerosis

Ravnik‐Glavač, M., & Glavač, D. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21051714 · Cited by 68 · see citing works

Integrated transcriptome landscape of ALS identifies genome instability linked to TDP-43 pathology

Ziff, O. J., Neeves, J., Mitchell, J. S., Tyzack, G. E., Martínez‐Ruiz, C., Luisier, R., Chakrabarti, A. M., McGranahan, N., Litchfield, K., Boulton, S. J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37630-6 · Cited by 69 · see citing works

Exciting Complexity: The Role of Motor Circuit Elements in ALS Pathophysiology

Gunes, Z. I., Kan, V. W. Y., Ye, X., & Liebscher, S. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00573 · Cited by 67 · see citing works

Lost in local translation: TDP-43 and FUS in axonal/neuromuscular junction maintenance and dysregulation in amyotrophic lateral sclerosis

Piol, D., Robberechts, T., & Cruz, S. D. (2023) · Neuron · doi:10.1016/j.neuron.2023.02.028 · Cited by 65 · see citing works

Interactions between gut microbiota and skeletal muscle

Gizard, F., Fernandez, A., & Vadder, F. D. (2020) · Nutrition and Metabolic Insights · doi:10.1177/1178638820980490 · Cited by 67 · see citing works

Spatial transcriptomics in development and disease

Zhou, R., Yang, G., Zhang, Y., & Wang, Y. (2023) · Molecular Biomedicine · doi:10.1186/s43556-023-00144-0 · Cited by 70 · see citing works

Molecular Mechanisms Underlying Neuroinflammation Elicited by Occupational Injuries and Toxicants

Pathak, D., & Sriram, K. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032272 · Cited by 69 · see citing works

Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples

Mirzazadeh, R., Andrusivová, Ž., Larsson, L., Newton, P. T., Galicia, L. A., Abalo, X. M., Avijgan, M., Kvastad, L., Denadai‐Souza, A., Stakenborg, N., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36071-5 · Cited by 68 · see citing works

Expansion spatial transcriptomics

Fan, Y., Andrusivová, Ž., Wu, Y., Chai, C., Larsson, L., He, M., Luo, L., Lundeberg, J., & Wang, B. (2023) · Nature Methods · doi:10.1038/s41592-023-01911-1 · Cited by 68 · see citing works

Spatial transcriptomics in neuroscience

Jung, N., & Kim, T.-K. (2023) · Experimental & Molecular Medicine · doi:10.1038/s12276-023-01093-y · Cited by 64 · see citing works

Cell type-specific changes identified by single-cell transcriptomics in Alzheimer’s disease

Luquez, T., Gaur, P., Kosater, I. M., Lam, M., Lee, D. I., Mares, J. A., Paryani, F., Yadav, A., & Menon, V. (2022) · Genome Medicine · doi:10.1186/s13073-022-01136-5 · Cited by 59 · see citing works

Spreading in ALS: The relative impact of upper and lower motor neuron involvement

Gromicho, M., Figueiral, M., Uysal, H., Großkreutz, J., Kuźma‐Kozakiewicz, M., Pinto, S., Petri, S., Madeira, S. C., Swash, M., & de Carvalho, M. A. (2020) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.51098 · Cited by 59 · see citing works

From Multi-Omics Approaches to Precision Medicine in Amyotrophic Lateral Sclerosis

Morello, G., Salomone, S., D’Agata, V., Conforti, F. L., & Cavallaro, S. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.577755 · Cited by 58 · see citing works

Emerging artificial intelligence applications in Spatial Transcriptomics analysis

Li, Y., Stanojevic, S., & Garmire, L. X. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.05.056 · Cited by 54 · see citing works

Spatial intra-tumor heterogeneity is associated with survival of lung adenocarcinoma patients

Wu, H., Temko, D., Maliga, Z., Moreira, A. L., Sei, E., Minussi, D. C., Dean, J., Lee, C., Xu, Q., Hochart, G., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100165 · Cited by 54 · see citing works

Interplay between immunity and amyotrophic lateral sclerosis: Clinical impact

Marchi, F. D., Munitić, I., Amedei, A., Berry, J. D., Feldman, E. L., Aronica, E., Nardo, G., Weehaeghe, D. V., Niccolai, E., Prtenjača, N., et al. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.06.027 · Cited by 52 · see citing works

Spatiotemporal gene expression in ALS

Zahn, L. M. (2019) · Science · doi:10.1126/science.364.6435.38-o

A multi-omics characterization reveals distinct molecular signatures in the human motor cortex and lumbar spinal cord in ALSPreprint

Barretto, N., Fullerton, B. T., Daly, A. C., Casel, O., Kuksenko, O., Kang, K., Petrescu, J., Xia, M., Eschbach, J., Leung, M., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.24.740361

Spinal cord molecular profiling provides a better understanding of Amyotrophic Lateral Sclerosis pathogenesis

Malaspina, A., & de Belleroche, J. (2004) · Brain Research Reviews · doi:10.1016/j.brainresrev.2004.04.002 · Cited by 36 · see citing works

Integrative transcriptomic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes

Humphrey, J., Venkatesh, S., Hasan, R., Herb, J. T., de Paiva Lopes, K., Küçükali, F., Byrska-Bishop, M., Evani, U. S., Narzisi, G., Fagegaltier, D., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01205-3 · Cited by 138 · see citing works

Microarray Analysis in Spinal Cords of Sporadic ALS Patients with Cell-Type Specific Transcriptome (P05.168)

Yamashita, H., Fujimori, N., Ito, H., Iguchi, Y., Atsuta, N., Tanaka, F., Sobue, G., Takahashi, R., & Yamanaka, K. (2012) · Neurology · doi:10.1212/wnl.78.1_meetingabstracts.p05.168