Opto-fluidic Instrument Runtime Estimation via Dynamic Z-bounds
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Solution Overview
Problem
Existing imaging techniques for biological samples, such as those used in spatial transcriptomics, often have fixed runtimes and completion times, which can be inefficient and do not adapt to the variability in sample characteristics.
Innovation Solution
The described techniques allow for the determination of Z-bounds of a sample based on a signal of interest, enabling the computation and display of variable runtimes and completion times for sample imaging. This is achieved through an opto-fluidic instrument that performs probing cycles, with parameters such as sample thickness and imageable volume being updated based on real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed runtimes are used for imaging tasks, then the process is simple to implement, but the efficiency and adaptability to sample characteristics deteriorate
Solution Approach 1:
The system performs preliminary actions by measuring sample parameters (thickness, Z-bounds) before the imaging process starts. These preliminary measurements are used to calculate and set the optimal runtime for the imaging task, ensuring the runtime is tailored to the specific sample characteristics rather than using a fixed default value.
Solution Approach 2:
The system implements feedback mechanisms where the measured sample parameters and calculated runtimes are monitored and adjusted during the imaging process. The progress indicator displays real-time information about the imaging progress and estimated completion time, allowing for dynamic adaptation based on actual imaging conditions and sample characteristics.
2Productivity
If variable runtimes are determined based on sample parameters, then the imaging efficiency improves, but the complexity of determining and managing runtimes increases
Solution Approach 1:
The system changes the runtime parameter dynamically based on measured sample parameters such as thickness and Z-bounds. By calculating the runtime as a function of these physical parameters, the system achieves variable imaging times that optimize efficiency for each specific sample without requiring complex manual configuration.
Solution Approach 2:
The system performs self-service by automatically measuring its own parameters (sample thickness, Z-bounds) and using these measurements to determine the appropriate runtime. This self-determination eliminates the need for external intervention to set runtime parameters, reducing operational complexity while maintaining high imaging efficiency.
3Loss of time
If conventional imaging techniques are used, then the equipment is simple, but the completion time cannot be accurately estimated or reduced
Solution Approach 1:
The system replaces mechanical or manual measurement methods with optical measurement techniques to determine sample Z-bounds and thickness. This substitution enables more precise and automated parameter measurement, which directly improves the accuracy of runtime estimation and reduces overall imaging time without requiring complex mechanical adjustment systems.
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
The approach results in more accurate and efficient imaging processes, as the runtimes and completion times can be dynamically adjusted to match the specific characteristics of the sample, potentially reducing overall imaging time and improving data quality.
Implementation Method 1
an opto-fluidic instrument to perform a plurality of probing cycles on the sample. Each of the probing cycles may include use of a plurality of fluorescent probes configured to bind to an analyte within the sample and emit a detectable optical signal upon excitation
Implementation Method 2
Each of the probing cycles may include use of a plurality of fluorescent probes configured to bind to an analyte within the sample and emit a detectable optical signal upon excitation
Implementation Method 3
a different set of barcode probes (e.g., fluorescently-labeled oligonucleotides) is contacted with target analytes (e.g., mRNA sequences) or with target barcodes (e.g., nucleic acid barcodes) associated with the target analytes present in a sample under conditions that promote hybridization
Data Source
AI summary
Based on at least one parameter, a first completion time window for performing a plurality of probing cycles on a sample in an opto-fluidic instrument may be determined. The at least one parameter may be associated with the sample and/or the opto-fluidic instrument. A progress indicator including the first completion time window may be generated for display. The opto-fluidic instrument may be caused to perform at least a first probing cycle of the plurality of probing cycles. The at least one parameter may be updated based on information obtained from at least the first probing cycle. Based on the updated at least one parameter, a second completion time window for the plurality of cycles of the opto-fluidic instrument may be determined. The second completion time window may be different than the first completion time window. An updated progress indicator including the second completion time window may be generated for display.