Two-Step Microorganism Detection via LDR and Microarray
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Solution Overview
Problem
Current methods for detecting and identifying microorganisms in samples are slow, laborious, and costly, often requiring extensive expertise and involving unnecessary steps, especially since most samples are negative for pathogens, leading to inefficiencies in clinical and food industry applications.
Innovation Solution
A two-step method using real-time ligase detection reaction (LDR) followed by microarray analysis with addressable identifier ZIP oligonucleotides for fast screening and identification of microorganisms, focusing on positive samples only to reduce unnecessary testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all samples are subjected to the entire process including microarray screening, then comprehensive detection is achieved, but time and resources are wasted on negative samples
Solution Approach 1:
The detection process is divided into two segments: a first rapid screening step using real-time PCR to identify samples containing microorganisms, and a second identification step using microarray analysis applied only to positive samples. This segmentation eliminates unnecessary processing of negative samples while ensuring comprehensive detection of true positives.
Solution Approach 2:
A preliminary screening step is performed before the main microarray analysis. The real-time PCR screening is conducted first to pre-identify positive samples, so that only these samples proceed to the time-consuming microarray identification step, thereby saving overall processing time.
2Measurement precision
If comprehensive identification and characterization is performed on all samples, then accurate results are obtained, but cost increases significantly
Solution Approach 1:
The identification process is segmented into two stages with different levels of complexity and cost. The first stage uses cost-effective real-time PCR for rapid screening, and the second stage applies expensive microarray analysis only to confirmed positive samples, thereby optimizing resource allocation while maintaining identification accuracy.
Solution Approach 2:
Instead of performing full identification on all samples, the method applies partial action (screening only) to negative samples and full action (identification and characterization) only to positive samples, eliminating unnecessary expenditure on samples that do not contain microorganisms.
3Manufacturing precision
If classical methods are used for subtyping and antibiotic resistance determination, then detailed characterization is achieved, but the process becomes slow and laborious
Solution Approach 1:
Classical mechanical and manual methods for subtyping and antibiotic resistance determination are replaced with molecular biology techniques (real-time PCR and microarray analysis). This substitution automates the process, increases throughput, and maintains or improves characterization detail while significantly reducing manual labor and time requirements.
Solution Approach 2:
The detection parameters are changed from phenotypic observations (slow, manual interpretation) to molecular-level detections (fast, automated, high-throughput). By detecting specific DNA sequences and using fluorescent signals, the method achieves detailed characterization with much higher productivity.
4Measurement precision
If purification of samples is performed to obtain pure strains, then accurate typing is achieved, but the process becomes tedious and time-consuming
Solution Approach 1:
The invention extracts and detects specific molecular targets (DNA sequences) directly from the sample without requiring extraction of pure microbial cells or strains. By using species-specific and strain-specific nucleotide sequence detection, the method achieves accurate typing while eliminating the tedious purification step.
Solution Approach 2:
Molecular probes and primers serve as intermediaries that specifically bind to target DNA sequences in complex sample matrices. These intermediaries enable direct detection and typing of microorganisms in unpurified samples, bypassing the need for strain purification while maintaining typing accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces time and cost by efficiently detecting and characterizing microorganisms, optimizing resource use and providing timely results in clinical and food safety contexts.
Implementation Method 1
a first step comprises the detection of the presence of micro-organisms via a detector molecule, and detecting amplified target nucleic acids resulting from a ligase detection reaction
Implementation Method 2
monitoring the signal of said detector molecule and/or the modulation of the signal of said detector molecule, a modulation in the signal of said detector molecule indicating the presence of said target sequence
Implementation Method 3
in a second step comprises further identification and characterization of said micro-organism via a labelled primer, wherein said amplified target nucleic acids are detected on a microarray using addressable identifier ZIP oligonucleotides (ZipComcode (cZIP) or Zipcode (ZIP))
Data Source
AI summary
The present invention relates to a specific method accomplishing fast and specific detection, identification and characterization of contaminating micro-organisms in various samples. A method has been developed based on the detection of species-specific and/or strain-specific nucleotide sequences that are uniquely identified and amplified and subsequently detected on a microarray using addressable identifier ZIP oligonucleotides. By using a two step screening process, the method of the present invention enables in first instance the fast screening of a multitude of samples for the presence or the absence of specific micro-organisms in such samples, while in a second screening step the positive results of the first step are further processed to identify and characterize the detected micro-organisms.