Deep learning techniques and mathematical modeling allow 3D analysis of mitotic spindle dynamics.
Deep learning techniques and mathematical modeling allow 3D analysis of mitotic spindle dynamics.
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DOI:
10.1083/jcb.202111094
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发表时间:
2023-05-01
影响因子:
7.8
通讯作者:
Draviam, Viji M.
中科院分区:
文献类型:
--
作者:
Dang, David;Efstathiou, Christoforos;Sun, Dijue;Yue, Haoran;Sastry, Nishanth R.;Draviam, Viji M.
Spatial and temporal discontinuities in time-lapse movies frequently disrupt automation methods such as 3D object segmentation and object tracking. To overcome this hurdle, we introduced SpinX, an image analysis framework to combine deep learning and mathematical object modeling to track mitotic spindle movements in 3D. Time-lapse microscopy movies have transformed the study of subcellular dynamics. However, manual analysis of movies can introduce bias and variability, obscuring important insights. While automation can overcome such limitations, spatial and temporal discontinuities in time-lapse movies render methods such as 3D object segmentation and tracking difficult. Here, we present SpinX, a framework for reconstructing gaps between successive image frames by combining deep learning and mathematical object modeling. By incorporating expert feedback through selective annotations, SpinX identifies subcellular structures, despite confounding neighbor-cell information, non-uniform illumination, and variable fluorophore marker intensities. The automation and continuity introduced here allows the precise 3D tracking and analysis of spindle movements with respect to the cell cortex for the first time. We demonstrate the utility of SpinX using distinct spindle markers, cell lines, microscopes, and drug treatments. In summary, SpinX provides an exciting opportunity to study spindle dynamics in a sophisticated way, creating a framework for step changes in studies using time-lapse microscopy.
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影响因子:
12.3
作者:
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者:
Sabatini DM
DOI:
10.4161/cc.25671
发表时间:
2013-08-15
期刊:
Cell cycle (Georgetown, Tex.)
影响因子:
--
作者:
Corrigan AM;Shrestha RL;Zulkipli I;Hiroi N;Liu Y;Tamura N;Yang B;Patel J;Funahashi A;Donald A;Draviam VM
通讯作者:
Draviam VM
影响因子:
48
作者:
Caicedo JC;Cooper S;Heigwer F;Warchal S;Qiu P;Molnar C;Vasilevich AS;Barry JD;Bansal HS;Kraus O;Wawer M;Paavolainen L;Herrmann MD;Rohban M;Hung J;Hennig H;Concannon J;Smith I;Clemons PA;Singh S;Rees P;Horvath P;Linington RG;Carpenter AE
通讯作者:
Carpenter AE
影响因子:
3.6
作者:
Hassani, Hossein;Kreysing, Eva
通讯作者:
Kreysing, Eva
影响因子:
11.4
作者:
Draviam, V. M.;Shapiro, I.;Sorger, P. K.
通讯作者:
Sorger, P. K.