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 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

scholarly journals A Camera Sensors-Based System to Study Drug Effects on In Vitro Motility: The Case of PC-3 Prostate Cancer Cells

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1531 ◽  
Author(s):  
Maria Colomba Comes ◽  
Arianna Mencattini ◽  
Davide Di Giuseppe ◽  
Joanna Filippi ◽  
Michele D’Orazio ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cell Motility ◽  
Cancer Cells ◽  
Time Lapse ◽  
Prostate Cancer Cells ◽  
Lab On Chip ◽  
Time Lapse Microscopy ◽  
On Chip ◽  
Cell Trajectory ◽  
Camera Sensors

Cell motility is the brilliant result of cell status and its interaction with close environments. Its detection is now possible, thanks to the synergy of high-resolution camera sensors, time-lapse microscopy devices, and dedicated software tools for video and data analysis. In this scenario, we formulated a novel paradigm in which we considered the individual cells as a sort of sensitive element of a sensor, which exploits the camera as a transducer returning the movement of the cell as an output signal. In this way, cell movement allows us to retrieve information about the chemical composition of the close environment. To optimally exploit this information, in this work, we introduce a new setting, in which a cell trajectory is divided into sub-tracks, each one characterized by a specific motion kind. Hence, we considered all the sub-tracks of the single-cell trajectory as the signals of a virtual array of cell motility-based sensors. The kinematics of each sub-track is quantified and used for a classification task. To investigate the potential of the proposed approach, we have compared the achieved performances with those obtained by using a single-trajectory paradigm with the scope to evaluate the chemotherapy treatment effects on prostate cancer cells. Novel pattern recognition algorithms have been applied to the descriptors extracted at a sub-track level by implementing features, as well as samples selection (a good teacher learning approach) for model construction. The experimental results have put in evidence that the performances are higher when a further cluster majority role has been considered, by emulating a sort of sensor fusion procedure. All of these results highlighted the high strength of the proposed approach, and straightforwardly prefigure its use in lab-on-chip or organ-on-chip applications, where the cell motility analysis can be massively applied using time-lapse microscopy images.

scholarly journals Single Cell Analysis of Bistable Expression of Pathogenicity Island 1 and the Flagellar Regulon in Salmonella enterica

2021 ◽  
Vol 9 (2) ◽  
pp. 210
Author(s):  
María Antonia Sánchez-Romero ◽  
Josep Casadesús
Keyword(s):  
Cell Sorting ◽  
Salmonella Enterica ◽  
Single Cell Analysis ◽  
Pathogenicity Island ◽  
Time Lapse ◽  
Cell Analysis ◽  
Microscopy Observation ◽  
Time Lapse Microscopy ◽  
Flagellar Regulon ◽  
Invasion Assays

Bistable expression of the Salmonella enterica pathogenicity island 1 (SPI-1) and the flagellar network (Flag) has been described previously. In this study, simultaneous monitoring of OFF and ON states in SPI-1 and in the flagellar regulon reveals independent switching, with concomitant formation of four subpopulations: SPI-1OFF FlagOFF, SPI-1OFF FlagON, SPI-1ON FlagOFF, and SPI-1ON FlagON. Invasion assays upon cell sorting show that none of the four subpopulations is highly invasive, thus raising the possibility that FlagOFF cells might contribute to optimal invasion as previously proposed for SPI-1OFF cells. Time lapse microscopy observation indicates that expression of the flagellar regulon contributes to the growth impairment previously described in SPI-1ON cells. As a consequence, growth resumption in SPI-1ON FlagON cells requires switching to both SPI-1OFF and FlagOFF states.

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

Going Beyond 3-D Imaging: Automated 3-D Montaged image Analysis of Cytological Specimens

1996 ◽  
Vol 54 ◽  
pp. 282-283
Author(s):  
Badrinath Roysam ◽  
Hakan Ancin ◽  
Douglas E. Becker ◽  
Robert W. Mackin ◽  
Matthew M. Chestnut ◽  
...  
Keyword(s):  
Image Analysis ◽  
Structural Changes ◽  
Sampling Methods ◽  
Spatial Clustering ◽  
Three Dimensional ◽  
Field Of View ◽  
Cell Counting ◽  
Depth Of Field ◽  
Tissue Cell ◽  
Tissue Organization

This paper summarizes recent advances made by this group in the automated three-dimensional (3-D) image analysis of cytological specimens that are much thicker than the depth of field, and much wider than the field of view of the microscope. The imaging of thick samples is motivated by the need to sample large volumes of tissue rapidly, make more accurate measurements than possible with 2-D sampling, and also to perform analysis in a manner that preserves the relative locations and 3-D structures of the cells. The motivation to study specimens much wider than the field of view arises when measurements and insights at the tissue, rather than the cell level are needed.The term “analysis” indicates a activities ranging from cell counting, neuron tracing, cell morphometry, measurement of tracers, through characterization of large populations of cells with regard to higher-level tissue organization by detecting patterns such as 3-D spatial clustering, the presence of subpopulations, and their relationships to each other. Of even more interest are changes in these parameters as a function of development, and as a reaction to external stimuli. There is a widespread need to measure structural changes in tissue caused by toxins, physiologic states, biochemicals, aging, development, and electrochemical or physical stimuli. These agents could affect the number of cells per unit volume of tissue, cell volume and shape, and cause structural changes in individual cells, inter-connections, or subtle changes in higher-level tissue architecture. It is important to process large intact volumes of tissue to achieve adequate sampling and sensitivity to subtle changes. It is desirable to perform such studies rapidly, with utmost automation, and at minimal cost. Automated 3-D image analysis methods offer unique advantages and opportunities, without making simplifying assumptions of tissue uniformity, unlike random sampling methods such as stereology.12 Although stereological methods are known to be statistically unbiased, they may not be statistically efficient. Another disadvantage of sampling methods is the lack of full visual confirmation - an attractive feature of image analysis based methods.

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).

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