Time-lapse microscopy and image analysis of Escherichia coli cells in mother machines

2016 ◽  
pp. 49-68 ◽  
Author(s):  
Y. Yang ◽  
X. Song ◽  
A.B. Lindner
Keyword(s):  
Escherichia Coli ◽  
Image Analysis ◽  
Time Lapse ◽  
Time Lapse Microscopy ◽  
Escherichia Coli Cells

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

10.3791/61290 ◽  
2021 ◽  
Author(s):  
Einel A. Chaimovitz ◽  
Evgeniy Reznik ◽  
Mouna Habib ◽  
Netanel Korin ◽  
Ramez Daniel
Keyword(s):  
Escherichia Coli ◽  
Continuous Measurement ◽  
Time Lapse ◽  
Biological Noise ◽  
Time Lapse Microscopy

scholarly journals Quantification of Mouse Hematopoietic Progenitors’ Formation Using Time-lapse Microscopy and Image Analysis

BIO-PROTOCOL ◽  
2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Isabelle Bergiers ◽  
Christian Tischer ◽  
Özge Bölükbaşı ◽  
Christophe Lancrin
Keyword(s):  
Image Analysis ◽  
Time Lapse ◽  
Hematopoietic Progenitors ◽  
Time Lapse Microscopy

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

Time lapse microscopy observation of cellular structural changes and image analysis of drug treated cancer cells to characterize the cellular heterogeneity

2014 ◽  
Vol 30 (6) ◽  
pp. 724-734 ◽  
Author(s):  
Periasamy S. Vaiyapuri ◽  
Alshatwi A. Ali ◽  
Akbarsha A. Mohammad ◽  
Jeyalakshmi Kandhavelu ◽  
Meenakshisundaram Kandhavelu
Keyword(s):  
Image Analysis ◽  
Cancer Cells ◽  
Structural Changes ◽  
Time Lapse ◽  
Cellular Heterogeneity ◽  
Microscopy Observation ◽  
Time Lapse Microscopy

scholarly journals TLM-Converter: reorganization of long time-lapse microscopy datasets for downstream image analysis

BioTechniques ◽  
2011 ◽  
Vol 51 (1) ◽  
Author(s):  
Wee Choo Puah ◽  
Leong Poh Cheok ◽  
Maté Biro ◽  
Wee Thong Ng ◽  
Martin Wasser
Keyword(s):  
Image Analysis ◽  
Time Lapse ◽  
Time Lapse Microscopy ◽  
Long Time

scholarly journals Functional Analysis of Human Microtubule-based Motor Proteins, the Kinesins and Dyneins, in Mitosis/Cytokinesis Using RNA Interference

2005 ◽  
Vol 16 (7) ◽  
pp. 3187-3199 ◽  
Author(s):  
Changjun Zhu ◽  
Jian Zhao ◽  
Marina Bibikova ◽  
Joel D. Leverson ◽  
Ella Bossy-Wetzel ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Functional Analysis ◽  
Motor Proteins ◽  
Time Lapse ◽  
Rnase Iii ◽  
Immunofluorescence Analysis ◽  
Cellular Processes ◽  
Time Lapse Microscopy ◽  
Chromosome Congression ◽  
Spindle Dynamics

Microtubule (MT)-based motor proteins, kinesins and dyneins, play important roles in multiple cellular processes including cell division. In this study, we describe the generation and use of an Escherichia coli RNase III-prepared human kinesin/dynein esiRNA library to systematically analyze the functions of all human kinesin/dynein MT motor proteins. Our results indicate that at least 12 kinesins are involved in mitosis and cytokinesis. Eg5 (a member of the kinesin-5 family), Kif2A (a member of the kinesin-13 family), and KifC1 (a member of the kinesin-14 family) are crucial for spindle formation; KifC1, MCAK (a member of the kinesin-13 family), CENP-E (a member of the kinesin-7 family), Kif14 (a member of the kinesin-3 family), Kif18 (a member of the kinesin-8 family), and Kid (a member of the kinesin-10 family) are required for chromosome congression and alignment; Kif4A and Kif4B (members of the kinesin-4 family) have roles in anaphase spindle dynamics; and Kif4A, Kif4B, MKLP1, and MKLP2 (members of the kinesin-6 family) are essential for cytokinesis. Using immunofluorescence analysis, time-lapse microscopy, and rescue experiments, we investigate the roles of these 12 kinesins in detail.

scholarly journals Microfluidic Cultivation and Laser Tweezers Raman Spectroscopy of E. coli under Antibiotic Stress

2018 ◽  
Author(s):  
Zdeněk Pilát ◽  
Silvie Bernatová ◽  
Jan Ježek ◽  
Johanna Kirchhoff ◽  
Astrid Tannert ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Raman Spectroscopy ◽  
Raman Spectra ◽  
Cell Elongation ◽  
Time Lapse ◽  
Laser Tweezers ◽  
E Coli ◽  
Cellular Physiology ◽  
Time Lapse Microscopy ◽  
Metabolic States

Analyzing the cells in various body fluids can greatly deepen the understanding of the mechanisms governing the cellular physiology. Because of the variability of physiological and metabolic states, it is important to be able to perform such studies on individual cells. Therefore, we developed an optofluidic system in which we precisely manipulated and monitored individual cells of Escherichia coli. We used laser tweezers Raman spectroscopy (LTRS) in a microchamber chip to manipulate and analyze individual E. coli cells. We subjected the cells to antibiotic cefotaxime, and we observed the changes by the time-lapse microscopy and Raman spectroscopy. We found observable changes in the cellular morphology (cell elongation) and in Raman spectra, which were consistent with other recently published observations. We tested the capabilities of the optofluidic system and found it to be a reliable and versatile solution for this class of microbiological experiments.

Experience of using time-lapse microscopy in IVF and ICSI programs

2020 ◽  
pp. 47-50
Author(s):  
N. V. Saraeva ◽  
N. V. Spiridonova ◽  
M. T. Tugushev ◽  
O. V. Shurygina ◽  
A. I. Sinitsyna
Keyword(s):  
Pregnancy Rate ◽  
Embryo Transfer ◽  
Selection Procedure ◽  
Time Lapse ◽  
Single Embryo Transfer ◽  
Main Group ◽  
Clinical Pregnancy ◽  
Control Group ◽  
Time Lapse Microscopy

In order to increase the pregnancy rate in the assisted reproductive technology, the selection of one embryo with the highest implantation potential it is very important. Time-lapse microscopy (TLM) is a tool for selecting quality embryos for transfer. This study aimed to assess the benefits of single-embryo transfer of autologous oocytes performed on day 5 of embryo incubation in a TLM-equipped system in IVF and ICSI programs. Single-embryo transfer following incubation in a TLM-equipped incubator was performed in 282 patients, who formed the main group; the control group consisted of 461 patients undergoing single-embryo transfer following a traditional culture and embryo selection procedure. We assessed the quality of transferred embryos, the rates of clinical pregnancy and delivery. The groups did not differ in the ratio of IVF and ICSI cycles, average age, and infertility factor. The proportion of excellent quality embryos for transfer was 77.0% in the main group and 65.1% in the control group (p = 0.001). In the subgroup with receiving eight and less oocytes we noted the tendency of receiving more quality embryos in the main group (р = 0.052). In the subgroup of nine and more oocytes the quality of the transferred embryos did not differ between two groups. The clinical pregnancy rate was 60.2% in the main group and 52.9% in the control group (p = 0.057). The delivery rate was 45.0% in the main group and 39.9% in the control group (p > 0.050).

Sign in / Sign up

Export Citation Format

Share Document