Microarray Chip Application Process and Data Analysis

Microarray chip refers to the use of photoconductive in-situ synthesis or micro spotting to solidify a large number of biological macromolecules such as nucleic acid fragments, polypeptide molecules, even tissue slices, cells and other biological samples on supports (such as slides, nylon membranes, etc. and other carriers), form a dense two-dimensional molecular arrangement, and then react with the target molecules in the labeled biological sample to be tested, and the intensity of the reaction signal is quickly measured by a specific instrument, for example, a laser confocal scanner or a charge-coupled camera can detect and analyze the intensity of the reaction signal quickly, in parallel, and efficiently, so as to determine the number of target molecules in the sample.

1. Application process of microarray chips

(1) Preparation of target

Nucleotides are extracted and labeled from biological samples.

(2) hybridization

Incubate the target with the cDNA or oligonucleotide sequence on the chip.

(3) Get data

The signal intensity exhibited by the target hybridized to the probe is scanned.

(4) Data analysis

Draw biologically meaningful conclusions from large amounts of data.

Microarray chip technology reflects the transcription of the gene expressing the mRNA by measuring the number of mRNA that can hybridize with the probe. The construction of the chip firstly selects the gene and the corresponding probe according to the needs of the research, and then extracts the mRNA from the sample. , and prepare the target, then add the target to the chip, perform operations such as incubating and hybridizing, washing out non-hybridized samples, and scanning to obtain raw data, and then standardize and statistically analyze these data to draw conclusions and construct appropriate microchips. Array chips are the basis for subsequent research.

2. Microarray chip data analysis

The advantage of microarray chip analysis is that it can scan a large number of genes of interest at the same time, but this is also the bottleneck of its research. An experiment will generate a large amount of data. How to analyze these data and draw biologically meaningful conclusions is an important issue for the further development and improvement of microarray chip technology. In this regard, computer technology and various statistical methods are needed. There has not been a large-scale comparative study among the various mathematical models currently used, so their effectiveness cannot be fully and comprehensively evaluated.

At present, in medical research, data analysis methods are generally divided into two categories: cluster analysis without supervision and cluster analysis with supervision. The former simply groups genes according to the similarity of gene expression from a mathematical point of view, which helps to discover new target genes or provide new disease information, such as new types, factors affecting prognosis, etc. The latter needs to be analyzed in conjunction with existing expertise and is suitable for disease classification. This is a useful supplement to traditional diagnostic methods.

In addition, in many current microarray chip studies, the number of genes studied each time is large, and the sample size involved in the experiment is small. This phenomenon is not conducive to obtaining stable and reproducible experimental results.