An alternative to culture-based cell death detection is an assessment of other cell viability indicators using fluorescent dyes, including membrane potential and membrane integrity. Live/Dead assays differentiates live and dead cells using membrane integrity as a proxy for cell viability and are based on a fluorescent staining procedure followed by detection using flow cytometry. However, samples preparation for such flow cytometry-based techniques could be challenging. Cell harvesting by trypsinization, mechanical or enzymatic cell disaggregation from tissues, extensive centrifugation steps, may all lead to preferential loss of apoptotic cells. To overcome this strictly follow manufacturers instruction of the detection kit.
An alternative to culture-based cell death detection is an assessment of other cell viability indicators using fluorescent dyes, including membrane potential and membrane integrity. Live/Dead assays differentiates live and dead cells using membrane integrity as a proxy for cell viability and are based on a fluorescent staining procedure followed by detection using flow cytometry. However, samples preparation for such flow cytometry-based techniques could be challenging. Cell harvesting by trypsinization, mechanical or enzymatic cell disaggregation from tissues, extensive centrifugation steps, may all lead to preferential loss of apoptotic cells. To overcome this strictly follow manufacturers instruction of the detection kit.
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.
DNA 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.
DNA 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.
DNA 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.
DNA 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.
DNA 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.
DNA 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.
RNA-Seq is a method to sequence RNA by applying Next Generation Sequencing (NGS). The quality of RNA is critical for the success of RNA-Seq. The integrity of RNA is measured by the RNA integrity number (RIN). RIN is computed from RNA electrophoresis and electropherogram profiles (the peak area of the 28S rRNA should be approximately twice the peak area of the 18S rRNA). If you get the RIN value lower than 7, the possibility of getting the low quality of RNA-seq data is high. To get a high quality RNA, it is better to work with fresh samples or snap-freeze the tissues in liquid nitrogen as quickly as possible and store them at -80°C until further use. Make sure designated areas and all your filter tips, microfuge tubes, plastic, and glassware are RNase-free.
Fill out your contact details and receive price quotes in your Inbox
Outsource experiment