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Bright field microscopic cells counting method for BEVS using nonlinear convergence index sliding band filter.

Sui D, Wang K, Park H, Chae J - Biomed Eng Online (2014)

Bottom Line: An effective host cell line cultivation protocol can facilitate the baculovirus preparation and following experiments.However, the counting of the number of host cells in the protocol is usually performed by manual observation with microscopy, which is time consuming and labor intensive work, and prone to errors for one person or between different individuals.This study aims at giving a bright field insect cells counting protocol to help improve the efficient of BEVS.

View Article: PubMed Central - PubMed

Affiliation: Biocomputing Research Center, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. wangkq@hit.edu.cn.

ABSTRACT

Background: The Baculovirus Expression Vector System (BEVS) is a very popular expression vector system in gene engineering. An effective host cell line cultivation protocol can facilitate the baculovirus preparation and following experiments. However, the counting of the number of host cells in the protocol is usually performed by manual observation with microscopy, which is time consuming and labor intensive work, and prone to errors for one person or between different individuals. This study aims at giving a bright field insect cells counting protocol to help improve the efficient of BEVS.

Method: To develop a reliable and accurate counting method for the host cells in the bright field, such as Sf9 insect cells, a novel method based on a nonlinear Transformed Sliding Band Filter (TSBF) was proposed. And 3 collaborators counted cells at the same time to produce the ground truth for evaluation. The performance of TSBF method was evaluated with the image datasets of Sf9 insect cells according to the different periods of cell cultivation on the cell density, error rate and growth curve.

Results: The average error rate of our TSBF method is 2.21% on average, ranging from 0.89% to 3.97%, which exhibited an excellent performance with its high accuracy in lower error rate compared with traditional methods and manual counting. And the growth curve was much the manual method well.

Conclusion: Results suggest the proposed TSBF method can detect insect cells with low error rate, and it is suitable for the counting task in BEVS to take the place of manual counting by humans. Growth curve results can reflect the cells' growth manner, which was generated by our proposed TSBF method in this paper can reflected the similar manner with it's from the manual method. All of these proven that the proposed insect cell counting method can clearly improve the efficiency of BEVS.

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Related in: MedlinePlus

Sf9 insect cell detection comparison between TSBF and SBF method. (a) TSBF detecting result on the 3rd day. (b) TSBF detecting result on the 5th day. (c) Magnification of part of (b). (d) SBF detecting result on the 3rd day. (e) SBF detecting result on the 5th day. (f) Magnification of part of (e).
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Fig6: Sf9 insect cell detection comparison between TSBF and SBF method. (a) TSBF detecting result on the 3rd day. (b) TSBF detecting result on the 5th day. (c) Magnification of part of (b). (d) SBF detecting result on the 3rd day. (e) SBF detecting result on the 5th day. (f) Magnification of part of (e).

Mentions: Unlike the dark field, insect cells in the bright field usually shows the whole living cell body and the cytoplasm is not visible compared with the cell membrane. Figure 5(d) shows the insect cells in the bright field compared with the retinal cells in the dark field (Figure 5(a)). The boundaries of an insect cell in the bright field shows a clearly boundaries of cytoplasm and the edge exhibited discontinuity between inside and outside of the cell. The gradient vector around the cell membrane area shows convergence toward the cytoplasm center and divergence away from the cell membrane toward the background, and this was modeled as a transformed rounded convex region enhanced by our proposed method discussed in part B. Figure 5(e) indicated the gradient vector distribution inside and outside of the insect cells. Unfortunately, as shown in Figure 5(f), the SBF filter does not work in the bright field, and it can only detect the region of the cell membrane instead. Figure 6(a) to (f) exhibited the detection results of comparison between the TSBF and SBF method in the 3rd and 5th day. TSBF can detect the center of each cell as one, but the SBF can only detect the cell membrane region and it detect one cell as two, three or even more cells.Figure 6


Bright field microscopic cells counting method for BEVS using nonlinear convergence index sliding band filter.

Sui D, Wang K, Park H, Chae J - Biomed Eng Online (2014)

Sf9 insect cell detection comparison between TSBF and SBF method. (a) TSBF detecting result on the 3rd day. (b) TSBF detecting result on the 5th day. (c) Magnification of part of (b). (d) SBF detecting result on the 3rd day. (e) SBF detecting result on the 5th day. (f) Magnification of part of (e).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4221726&req=5

Fig6: Sf9 insect cell detection comparison between TSBF and SBF method. (a) TSBF detecting result on the 3rd day. (b) TSBF detecting result on the 5th day. (c) Magnification of part of (b). (d) SBF detecting result on the 3rd day. (e) SBF detecting result on the 5th day. (f) Magnification of part of (e).
Mentions: Unlike the dark field, insect cells in the bright field usually shows the whole living cell body and the cytoplasm is not visible compared with the cell membrane. Figure 5(d) shows the insect cells in the bright field compared with the retinal cells in the dark field (Figure 5(a)). The boundaries of an insect cell in the bright field shows a clearly boundaries of cytoplasm and the edge exhibited discontinuity between inside and outside of the cell. The gradient vector around the cell membrane area shows convergence toward the cytoplasm center and divergence away from the cell membrane toward the background, and this was modeled as a transformed rounded convex region enhanced by our proposed method discussed in part B. Figure 5(e) indicated the gradient vector distribution inside and outside of the insect cells. Unfortunately, as shown in Figure 5(f), the SBF filter does not work in the bright field, and it can only detect the region of the cell membrane instead. Figure 6(a) to (f) exhibited the detection results of comparison between the TSBF and SBF method in the 3rd and 5th day. TSBF can detect the center of each cell as one, but the SBF can only detect the cell membrane region and it detect one cell as two, three or even more cells.Figure 6

Bottom Line: An effective host cell line cultivation protocol can facilitate the baculovirus preparation and following experiments.However, the counting of the number of host cells in the protocol is usually performed by manual observation with microscopy, which is time consuming and labor intensive work, and prone to errors for one person or between different individuals.This study aims at giving a bright field insect cells counting protocol to help improve the efficient of BEVS.

View Article: PubMed Central - PubMed

Affiliation: Biocomputing Research Center, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. wangkq@hit.edu.cn.

ABSTRACT

Background: The Baculovirus Expression Vector System (BEVS) is a very popular expression vector system in gene engineering. An effective host cell line cultivation protocol can facilitate the baculovirus preparation and following experiments. However, the counting of the number of host cells in the protocol is usually performed by manual observation with microscopy, which is time consuming and labor intensive work, and prone to errors for one person or between different individuals. This study aims at giving a bright field insect cells counting protocol to help improve the efficient of BEVS.

Method: To develop a reliable and accurate counting method for the host cells in the bright field, such as Sf9 insect cells, a novel method based on a nonlinear Transformed Sliding Band Filter (TSBF) was proposed. And 3 collaborators counted cells at the same time to produce the ground truth for evaluation. The performance of TSBF method was evaluated with the image datasets of Sf9 insect cells according to the different periods of cell cultivation on the cell density, error rate and growth curve.

Results: The average error rate of our TSBF method is 2.21% on average, ranging from 0.89% to 3.97%, which exhibited an excellent performance with its high accuracy in lower error rate compared with traditional methods and manual counting. And the growth curve was much the manual method well.

Conclusion: Results suggest the proposed TSBF method can detect insect cells with low error rate, and it is suitable for the counting task in BEVS to take the place of manual counting by humans. Growth curve results can reflect the cells' growth manner, which was generated by our proposed TSBF method in this paper can reflected the similar manner with it's from the manual method. All of these proven that the proposed insect cell counting method can clearly improve the efficiency of BEVS.

Show MeSH
Related in: MedlinePlus