A rapid and accurate method to quantify neurite outgrowth from cell and tissue cultures: Two image analytic approaches using adaptive thresholds or machine learning

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Fältnamn	Indikatorer	Metadata
000		04067naa a2200421 4500
001		oai:DiVA.org:uu-397193
003		SwePub
008		191118s2020 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-3971932 URI
024	7	a https://doi.org/10.1016/j.jneumeth.2019.1085222 DOI
040		a (SwePub)uu
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a Ossinger, Alexanderu Uppsala universitet,Ortopedi4 aut0 (Swepub:uu)aleos222
245	1 0	a A rapid and accurate method to quantify neurite outgrowth from cell and tissue cultures :b Two image analytic approaches using adaptive thresholds or machine learning
264	1	b Elsevier BV,c 2020
338		a print2 rdacarrier
500		a De 2 första författarna delar förstaförfattarskapet
520		a BACKGROUND: 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.NEW METHOD: We 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.RESULTS: NeuriteSegmentation 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.COMPARISON WITH EXISTING METHODS: The methods were positively correlated and were more time-saving than manual counts, having performing times varying from 0.5-2 minutes. In addition, NeuriteSegmentation was compared to NeuriteJ®, that uses global thresholding, being more reliable in recognizing axons in areas of intense background.CONCLUSION: The 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.
650	7	a MEDICIN OCH HÄLSOVETENSKAPx Medicinska och farmaceutiska grundvetenskaperx Cell- och molekylärbiologi0 (SwePub)301082 hsv//swe
650	7	a MEDICAL AND HEALTH SCIENCESx Basic Medicinex Cell and Molecular Biology0 (SwePub)301082 hsv//eng
653		a Adaptive threshold
653		a Axonal outgrowth
653		a Global threshold
653		a Machine learning
653		a Ramification index
700	1	a Bajic, Andreju Uppsala universitet,Ortopedi4 aut0 (Swepub:uu)andba209
700	1	a Pan, Su Uppsala universitet,Ortopedi4 aut
700	1	a Andersson, Brittmarieu Uppsala universitet,Ortopedi4 aut0 (Swepub:uu)brand516
700	1	a Ranefall, Petter,d 1968-u Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion,Science for Life Laboratory, SciLifeLab4 aut0 (Swepub:uu)peran517
700	1	a Hailer, Nils P.u Uppsala universitet,Ortopedi4 aut0 (Swepub:uu)nilha837
700	1	a Schizas, Nikos,d 1979-u Uppsala universitet,Ortopedi4 aut0 (Swepub:uu)niksc983
710	2	a Uppsala universitetb Ortopedi4 org
773	0	t Journal of Neuroscience Methodsd : Elsevier BVg 331q 331x 0165-0270x 1872-678X
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-397193
856	4 8	u https://doi.org/10.1016/j.jneumeth.2019.108522

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Av författaren/redakt...: Ossinger, Alexan ...; Bajic, Andrej; Pan, S; Andersson, Britt ...; Ranefall, Petter ...; Hailer, Nils P.; visa fler...; Schizas, Nikos, ...; visa färre...

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