{"entity": "journal", "iuid": "271f9a2ca76d4508bf30335370c10fb0", "timestamp": "2026-09-26T23:02:18.566Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/journal/J%20Neurosci%20Methods.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/journal/J%20Neurosci%20Methods"}}, "title": "J Neurosci Methods", "issn": "1872-678X", "issn-l": null, "publications_count": 2, "publications": [{"entity": "publication", "iuid": "5ae7cd47108047ca8b8ca496fe309ad1", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/5ae7cd47108047ca8b8ca496fe309ad1.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/5ae7cd47108047ca8b8ca496fe309ad1"}}, "title": "CUBIC-f: An optimized clearing method for cell tracing and evaluation of neurite density in the salamander brain.", "authors": [{"family": "Pinheiro", "given": "Tiago", "initials": "T"}, {"family": "Mayor", "given": "Ivy", "initials": "I"}, {"family": "Edwards", "given": "Steven", "initials": "S"}, {"family": "Joven", "given": "Alberto", "initials": "A"}, {"family": "Kantzer", "given": "Christina G", "initials": "CG"}, {"family": "Kirkham", "given": "Matthew", "initials": "M"}, {"family": "Simon", "given": "Andr\u00e1s", "initials": "A"}], "type": "journal article", "published": "2021-01-15", "journal": {"title": "J Neurosci Methods", "issn": "1872-678X", "volume": "348", "pages": "109002", "issn-l": null}, "abstract": "Although tissue clearing and subsequent whole-brain imaging is now possible, standard protocols need to be adjusted to the innate properties of each specific tissue for optimal results. This work modifies exiting protocols to clear fragile brain samples and documents a downstream pipeline for image processing and data analysis.\n\nWe developed a clearing protocol, CUBIC-f, which we optimized for fragile samples, such as the salamander brain. We modified hydrophilic and aqueous' tissue-clearing methods based on Advanced CUBIC by incorporating Omnipaque 350 for refractive index matching.\n\nBy combining CUBIC-f, light sheet microscopy and bioinformatic pipelines, we quantified neuronal cell density, traced genetically marked fluorescent cells over long distance, and performed high resolution characterization of neural progenitor cells in the salamander brain. We also found that CUBIC-f is suitable for conserving tissue integrity in embryonic mouse brains.\n\nCUBIC-f shortens clearing and staining times, and requires less reagent use than Advanced CUBIC and Advanced CLARITY.\n\nCUBIC-f is suitable for conserving tissue integrity in embryonic mouse brains, larval and adult salamander brains which display considerable deformation using traditional CUBIC and CLARITY protocols.", "doi": "10.1016/j.jneumeth.2020.109002", "pmid": "33217411", "labels": [], "xrefs": [{"db": "pii", "key": "S0165-0270(20)30425-8"}], "notes": [], "created": "2026-09-23T12:47:07.261Z", "modified": "2026-09-23T12:47:07.277Z"}, {"entity": "publication", "iuid": "1674212c849b4817a268f34fa95b0465", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/1674212c849b4817a268f34fa95b0465.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/1674212c849b4817a268f34fa95b0465"}}, "title": "A rapid and accurate method to quantify neurite outgrowth from cell and tissue cultures: Two image analytic approaches using adaptive thresholds or machine learning.", "authors": [{"family": "Ossinger", "given": "A", "initials": "A"}, {"family": "Bajic", "given": "A", "initials": "A"}, {"family": "Pan", "given": "S", "initials": "S"}, {"family": "Andersson", "given": "B", "initials": "B"}, {"family": "Ranefall", "given": "P", "initials": "P"}, {"family": "Hailer", "given": "N P", "initials": "NP"}, {"family": "Schizas", "given": "N", "initials": "N"}], "type": "journal article", "published": "2020-02-01", "journal": {"title": "J Neurosci Methods", "issn": "1872-678X", "volume": "331", "pages": "108522", "issn-l": null}, "abstract": "Assessments of axonal outgrowth and dendritic development are essential readouts in many in vitro models in the field of neuroscience. Available analysis software is based on the assessment of fixed immunolabelled tissue samples, making it impossible to follow the dynamic development of neurite outgrowth. Thus, automated algorithms that efficiently analyse brightfield images, such as those obtained during time-lapse microscopy, are needed.\n\nWe developed and validated algorithms to quantitatively assess neurite outgrowth from living and unstained spinal cord slice cultures (SCSCs) and dorsal root ganglion cultures (DRGCs) based on an adaptive thresholding approach called NeuriteSegmantation. We used a machine learning approach to evaluate dendritic development from dissociate neuron cultures.\n\nNeuriteSegmentation successfully recognized axons in brightfield images of SCSCs and DRGCs. The temporal pattern of axonal growth was successfully assessed. In dissociate neuron cultures the total number of cells and their outgrowth of dendrites were successfully assessed using machine learning.\n\nThe methods were positively correlated and were more time-saving than manual counts, having performing times varying from 0.5-2 min. In addition, NeuriteSegmentation was compared to NeuriteJ\u00ae, that uses global thresholding, being more reliable in recognizing axons in areas of intense background.\n\nThe developed image analysis methods were more time-saving and user-independent than established approaches. Moreover, by using adaptive thresholding, we could assess images with large variations in background intensity. These tools may prove valuable in the quantitative analysis of axonal and dendritic outgrowth from numerous in vitro models used in neuroscience.", "doi": "10.1016/j.jneumeth.2019.108522", "pmid": "31734324", "labels": [], "xrefs": [{"db": "pii", "key": "S0165-0270(19)30379-6"}], "notes": [], "created": "2026-09-23T12:41:07.299Z", "modified": "2026-09-23T12:41:07.381Z"}], "created": "2026-09-23T12:41:07.311Z", "modified": "2026-09-23T12:41:07.311Z"}