Contamination can affect cell characteristics, i.e., growth, metabolism, and morphology leading to unreliable and erroneous experimental data. Depending on the source of contaminants, one can detect contamination by using a light microscope, gram stain, isothermal amplification, or PCR. Bacteria and fungi can usually be identified by optical microscopy. Mycoplasma in cell cultures cannot be detected visually. Hence, these microbes can go unnoticed for long periods and are determined using dedicated assays. Early and rapid identification of contaminants is vital to detect, handle and prevent contamination for good cell-culture practices. However, detection and identification can be challenging and tricky based on usual visual identifications. Hence it is essential to use a standard contamination detection kit to detect and maintain best practices.
Contamination can affect cell characteristics, i.e., growth, metabolism, and morphology leading to unreliable and erroneous experimental data. Depending on the source of contaminants, one can detect contamination by using a light microscope, gram stain, isothermal amplification, or PCR. Bacteria and fungi can usually be identified by optical microscopy. Mycoplasma in cell cultures cannot be detected visually. Hence, these microbes can go unnoticed for long periods and are determined using dedicated assays. Early and rapid identification of contaminants is vital to detect, handle and prevent contamination for good cell-culture practices. However, detection and identification can be challenging and tricky based on usual visual identifications. Hence it is essential to use a standard contamination detection kit to detect and maintain best practices.
Contamination can affect cell characteristics, i.e., growth, metabolism, and morphology leading to unreliable and erroneous experimental data. Depending on the source of contaminants, one can detect contamination by using a light microscope, gram stain, isothermal amplification, or PCR. Bacteria and fungi can usually be identified by optical microscopy. Mycoplasma in cell cultures cannot be detected visually. Hence, these microbes can go unnoticed for long periods and are determined using dedicated assays. Early and rapid identification of contaminants is vital to detect, handle and prevent contamination for good cell-culture practices. However, detection and identification can be challenging and tricky based on usual visual identifications. Hence it is essential to use a standard contamination detection kit to detect and maintain best practices.
Microarrays enable researchers to monitor the expression of thousands of genes simultaneously. However, the sensitivity, accuracy, specificity, and reproducibility are major challenges for this technology. Cross-hybridization, combination with splice variants, is a prime source for the discrepancies in differential gene expression calls among various microarray platforms. Removing (either from production or downstream bioinformatic analysis) and/or redesigning the microarray probes prone to cross-hybridization is a reasonable strategy to increase the hybridization specificity and hence, the accuracy of the microarray measurements.
Microarrays enable researchers to monitor the expression of thousands of genes simultaneously. However, the sensitivity, accuracy, specificity, and reproducibility are major challenges for this technology. Cross-hybridization, combination with splice variants, is a prime source for the discrepancies in differential gene expression calls among various microarray platforms. Removing (either from production or downstream bioinformatic analysis) and/or redesigning the microarray probes prone to cross-hybridization is a reasonable strategy to increase the hybridization specificity and hence, the accuracy of the microarray measurements.
Microarrays enable researchers to monitor the expression of thousands of genes simultaneously. However, the sensitivity, accuracy, specificity, and reproducibility are major challenges for this technology. Cross-hybridization, combination with splice variants, is a prime source for the discrepancies in differential gene expression calls among various microarray platforms. Removing (either from production or downstream bioinformatic analysis) and/or redesigning the microarray probes prone to cross-hybridization is a reasonable strategy to increase the hybridization specificity and hence, the accuracy of the microarray measurements.
Microarrays enable researchers to monitor the expression of thousands of genes simultaneously. However, the sensitivity, accuracy, specificity, and reproducibility are major challenges for this technology. Cross-hybridization, combination with splice variants, is a prime source for the discrepancies in differential gene expression calls among various microarray platforms. Removing (either from production or downstream bioinformatic analysis) and/or redesigning the microarray probes prone to cross-hybridization is a reasonable strategy to increase the hybridization specificity and hence, the accuracy of the microarray measurements.
Microarrays enable researchers to monitor the expression of thousands of genes simultaneously. However, the sensitivity, accuracy, specificity, and reproducibility are major challenges for this technology. Cross-hybridization, combination with splice variants, is a prime source for the discrepancies in differential gene expression calls among various microarray platforms. Removing (either from production or downstream bioinformatic analysis) and/or redesigning the microarray probes prone to cross-hybridization is a reasonable strategy to increase the hybridization specificity and hence, the accuracy of the microarray measurements.
Microarrays enable researchers to monitor the expression of thousands of genes simultaneously. However, the sensitivity, accuracy, specificity, and reproducibility are major challenges for this technology. Cross-hybridization, combination with splice variants, is a prime source for the discrepancies in differential gene expression calls among various microarray platforms. Removing (either from production or downstream bioinformatic analysis) and/or redesigning the microarray probes prone to cross-hybridization is a reasonable strategy to increase the hybridization specificity and hence, the accuracy of the microarray measurements.
Microarrays enable researchers to monitor the expression of thousands of genes simultaneously. However, the sensitivity, accuracy, specificity, and reproducibility are major challenges for this technology. Cross-hybridization, combination with splice variants, is a prime source for the discrepancies in differential gene expression calls among various microarray platforms. Removing (either from production or downstream bioinformatic analysis) and/or redesigning the microarray probes prone to cross-hybridization is a reasonable strategy to increase the hybridization specificity and hence, the accuracy of the microarray measurements.
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