The molecular diagnosis platform achieves high-throughput processing for multi-gene parallel testing by optimizing hardware architecture, improving reagent performance, integrating automated processes, and upgrading data analysis algorithms. This multi-faceted technology collaboratively overcomes the throughput bottlenecks of traditional testing. Its core approach is to integrate sample processing, nucleic acid amplification, signal acquisition, and result interpretation into a standardized pipeline. It also leverages microfluidic chips, arrayed carriers, and high-density probe technology to maximize space and resource utilization.
On the hardware level, the molecular diagnosis platform combines regular array chips with microfluidic technology to create a high-density parallel detection unit. For example, silicon-based chips manufactured using semiconductor precision processing techniques can form millions of nanoscale binding sites on their surfaces. Each site immobilizes a single DNA nanosphere through positive and negative charge interactions, ensuring zero crosstalk between signal points. This design enables a single chip to process hundreds of samples simultaneously, with thousands of detection sites per sample, significantly improving space utilization. The accompanying automated pipetting system and fluidic control module enable unattended processing of the entire sample lysis, purification, and amplification process, minimizing manual errors.
In terms of reagent development, the molecular diagnosis platform utilizes glycerol-free, high-concentration enzyme preparations and frozen premix technology to optimize reaction system stability. The viscosity of glycerol in traditional reagents can lead to pipetting errors, while the new glycerol-free enzyme preparation enhances protein stability, enabling long-term storage at room temperature and compatibility with automated pipetting robots. The frozen premix integrates primers, probes, and buffers into a solid-state carrier, simplifying the workflow by simply adding the sample. Furthermore, the collaborative design of the hot-start polymerase and chemical buffer suppresses nonspecific amplification, maintaining high sensitivity even in crude samples containing PCR inhibitors.
In terms of detection technology, the molecular diagnosis platform integrates sequencing by synthesis and combined probe-anchored polymerization to achieve dynamic signal capture. For example, the MGI sequencing platform uses rolling circle amplification to generate DNA nanospheres. Combined with four-color fluorescently labeled nucleotides and a high-resolution imaging system, it can simultaneously read thousands of bases. For multi-gene testing, the platform utilizes liquid-phase microarray technology to immobilize specific probes on the microsphere surface, enabling flow cytometry to detect hundreds of indicators in a single reaction. The advantage of this technical approach is that it can directly analyze gene expression levels without amplification, avoiding PCR bias.
During the data analysis phase, the molecular diagnosis platform relies on parallel computing and machine learning algorithms to improve processing efficiency. Faced with massive amounts of sequencing data, the distributed computing system can split the task among multiple nodes, shortening analysis time. By training on a vast amount of known variant data, the machine learning model can automatically identify gene mutation types and distinguish between synonymous/nonsynonymous SNPs and frameshift mutations. For example, in multi-gene tumor testing, the algorithm can simultaneously analyze the mutation, rearrangement, and amplification status of hundreds of genes to generate personalized medication guidance reports.
A quality control system is implemented throughout the molecular diagnosis platform. Internal quality control monitors amplification efficiency and signal strength through the use of positive/negative controls and internal reference genes. External evaluations, including participation in international external quality assessment programs, ensure traceability of results. For example, metric learning is used to adjust the spatial distribution of sample features, ensuring comparability of test results across batches. Furthermore, an electronic event log system records the entire operation process, facilitating problem tracing and performance optimization.
In terms of expanding application scenarios, the molecular diagnosis platform has expanded beyond genetic disease screening to include early cancer screening, pathogen detection, and reproductive genetics. In cancer diagnosis and treatment, high-throughput PCR technology, through fluorescent encoding, enables single-tube 100-plex testing, achieving sensitivity comparable to digital PCR while reducing costs. In reproductive health, non-invasive prenatal testing, through analysis of cell-free placental DNA, can simultaneously detect dozens of chromosomal abnormalities, with coverage far exceeding traditional serological screening.
In the future, the molecular diagnosis platform will develop towards greater efficiency and integration. The integration of single-molecule sequencing technology and CRISPR testing may enable real-time genetic analysis without amplification. AI-driven automated laboratories will further shorten testing cycles and promote the widespread adoption of precision medicine in primary healthcare settings.
