Tracking neural stem cells in time-lapse microscopy image sequences

2005 ◽  
Author(s):  
Karin Althoff ◽  
Johan Degerman ◽  
Tomas Gustavsson
Keyword(s):  
Stem Cells ◽  
Neural Stem Cells ◽  
Time Lapse ◽  
Image Sequences ◽  
Time Lapse Microscopy ◽  
Microscopy Image

scholarly journals Live-cell time-lapse imaging and single-cell tracking of in vitro cultured neural stem cells – Tools for analyzing dynamics of cell cycle, migration, and lineage selection

Methods ◽  
2018 ◽  
Vol 133 ◽  
pp. 81-90 ◽  
Author(s):  
Katja M. Piltti ◽  
Brian J. Cummings ◽  
Krystal Carta ◽  
Ayla Manughian-Peter ◽  
Colleen L. Worne ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Stem Cells ◽  
Neural Stem Cells ◽  
Single Cell ◽  
Cell Tracking ◽  
Time Lapse ◽  
Live Cell ◽  
Time Lapse Imaging

scholarly journals Embryonic cortical neural stem cells migrate ventrally and persist as postnatal striatal stem cells

2006 ◽  
Vol 175 (1) ◽  
pp. 159-168 ◽  
Author(s):  
Sandrine Willaime-Morawek ◽  
Raewyn M. Seaberg ◽  
Claudia Batista ◽  
Etienne Labbé ◽  
Liliana Attisano ◽  
...  
Keyword(s):  
Stem Cells ◽  
Neural Stem Cells ◽  
Homeobox Gene ◽  
Time Lapse ◽  
Video Microscopy ◽  
Lineage Tracing ◽  
Cell Labeling ◽  
Germinal Zone ◽  
Direct Cell ◽  
Time Lapse Video

Embryonic cortical neural stem cells apparently have a transient existence, as they do not persist in the adult cortex. We sought to determine the fate of embryonic cortical stem cells by following Emx1IREScre; LacZ/EGFP double-transgenic murine cells from midgestation into adulthood. Lineage tracing in combination with direct cell labeling and time-lapse video microscopy demonstrated that Emx1-lineage embryonic cortical stem cells migrate ventrally into the striatal germinal zone (GZ) perinatally and intermingle with striatal stem cells. Upon integration into the striatal GZ, cortical stem cells down-regulate Emx1 and up-regulate Dlx2, which is a homeobox gene characteristic of the developing striatum and striatal neural stem cells. This demonstrates the existence of a novel dorsal-to-ventral migration of neural stem cells in the perinatal forebrain.

scholarly journals An Advanced Framework for Time-lapse Microscopy Image Analysis

2020 ◽  
Author(s):  
Qibing Jiang ◽  
Praneeth Sudalagunta ◽  
Mark B. Meads ◽  
Khandakar Tanvir Ahmed ◽  
Tara Rutkowski ◽  
...  
Keyword(s):  
Image Analysis ◽  
Drug Response ◽  
Time Lapse ◽  
Response To Therapy ◽  
Patient Specific ◽  
Microscopy Imaging ◽  
Time Lapse Microscopy ◽  
Link Type ◽  
Microscopy Image ◽  
Microscopy Image Analysis

ABSTRACTTime-lapse microscopy is a powerful technique that generates large volumes of image-based information to quantify the behaviors of cell populations. This method has been applied to cancer studies to estimate the drug response for precision medicine and has great potential to address inter-patient (or intertumoral) heterogeneity. A couple of algorithms exist to analyze time-lapse microscopy images; however, most deal with very high-resolution images involving few cells (typically cell lines). There are currently no advanced and efficient computational frameworks available to process large-scale time-lapse microscopy imaging data to estimate patient-specific response to therapy based on a large population of primary cells. In this paper, we propose a robust and user-friendly pipeline to preprocess the images and track the behaviors of thousands of cancer cells simultaneously for a better drug response prediction of cancer patients.Availability and ImplementationSource code is available at: https://github.com/CompbioLabUCF/CellTrackACM Reference FormatQibing Jiang, Praneeth Sudalagunta, Mark B. Meads, Khandakar Tanvir Ahmed, Tara Rutkowski, Ken Shain, Ariosto S. Silva, and Wei Zhang. 2020. An Advanced Framework for Time-lapse Microscopy Image Analysis. In Proceedings of BioKDD: 19th International Workshop on Data Mining In Bioinformatics (BioKDD). ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

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