Derivative works

Defensive Disclosure: Derivative Innovations for Analyte Detection by Temporal Barcoding

Publication Date: May 12, 2026
Reference Technology: U.S. Patent 11,566,277 ("Compositions and methods for analyte detection")
Objective: This document discloses a plurality of derivative inventions, alternative embodiments, and cross-domain applications of the core technology described in US 11,566,277. The purpose is to place these concepts in the public domain, thereby establishing them as prior art for patentability purposes.

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Derivative Set 1: Material & Component Substitution

1.1. Electrochemical Temporal Barcoding (ETB)

• Enabling Description: This variation replaces optical detection with an electrochemical readout. The nucleic acid label of the detection reagent is synthesized to include specific sites for binding decoder probes, as in the reference patent. However, each decoder probe is conjugated not to a fluorophore, but to a unique redox-active molecule (e.g., ferrocene, methylene blue, or a metal nanoparticle with a distinct redox potential). The sample is placed on a microelectrode array. In each cycle, a set of redox-labeled decoder probes is hybridized. A cyclic voltammetry (CV) or square wave voltammetry (SWV) scan is performed, generating a signal (a current peak at a specific voltage) for each redox molecule present. This "voltammogram" constitutes the signal signature for that cycle. The probes are then chemically or thermally stripped, and the next cycle begins. The temporal sequence of voltammograms provides the analyte's identifier.

• Mermaid Diagram:

  sequenceDiagram
      participant MEA as Microelectrode Array
      participant Sample as Sample Surface
      participant Reagent as Detection Reagent
      participant Decoder as Redox Decoder Probe
      participant Reader as Potentiostat
  
      MEA->>Sample: Provides detection surface
      Reagent->>Sample: Binds to target analyte
      loop Cycle 1
          Decoder->>Reagent: Hybridize (Ferrocene-labeled)
          Reader->>MEA: Apply Voltage Ramp (CV)
          MEA-->>Reader: Measure Current (Peak at V1)
          Decoder->>Reagent: Strip probe
      end
      loop Cycle 2
          Decoder->>Reagent: Hybridize (Methylene Blue-labeled)
          Reader->>MEA: Apply Voltage Ramp (CV)
          MEA-->>Reader: Measure Current (Peak at V2)
          Decoder->>Reagent: Strip probe
      end
      Reader->>System: Compile Temporal Voltammogram [V1, V2, ...]
  

1.2. Molecularly Imprinted Polymer (MIP) Probes with PNA Labels

• Enabling Description: This derivative replaces biological antibodies or aptamers with robust, synthetic molecularly imprinted polymers (MIPs). MIPs are created by polymerizing functional monomers around a template molecule (the analyte). After polymerization, the template is removed, leaving a cavity with high specificity for the analyte. These MIP nanoparticles serve as the probe reagent. The MIPs are conjugated to Peptide Nucleic Acid (PNA) labels. PNA has a neutral backbone, making its hybridization highly stable and resistant to enzymatic degradation and extreme pH, thus suitable for industrial or environmental samples. The temporal readout proceeds via hybridization with fluorescently-labeled DNA or PNA decoder probes as described in the reference patent, but leverages the superior stability of the PNA label backbone.

• Mermaid Diagram:

  flowchart TD
      subgraph Synthesis
          A[Analyte Template] --> B{Mix with Functional Monomers};
          B --> C{Polymerization};
          C --> D[Remove Template];
          D --> E(MIP Nanoparticle);
      end
      subgraph Conjugation
          E --> F{Activate Surface};
          G[PNA Barcode Label] --> H{Activate Terminus};
          F & H --> I[Conjugate MIP to PNA];
      end
      subgraph Assay
          I --> J(MIP-PNA Detection Reagent);
          J --> K{Apply to Sample};
          K --> L[Temporal Readout via Hybridization];
      end
  

Derivative Set 2: Operational Parameter Expansion

2.1. In-Situ Intracellular Transcriptome Mapping

• Enabling Description: This method scales the technology down to the nanoscale for use inside living or fixed cells. The detection reagents, targeting specific mRNA sequences, are encapsulated in lipid nanoparticles (LNPs) functionalized with cell-penetrating peptides. Upon entering a cell, the LNPs release the reagents. Each reagent's probe is a locked nucleic acid (LNA) oligonucleotide for high-affinity binding to its target mRNA. The attached DNA label is then detected temporally. The readout cycles are performed on a microscope stage with automated microfluidics. Decoder probes are modified to include a cell-permeable trans-activating transcriptional activator (TAT) peptide. This allows for repeated interrogation of the spatial location of individual mRNA molecules within the subcellular architecture, creating a 3D temporal map of the transcriptome.

• Mermaid Diagram:

  graph TD
      A[LNP Encapsulated Reagents] --> B{Cellular Uptake};
      B --> C[Reagent Release in Cytoplasm];
      C --> D{Probe binds target mRNA};
      subgraph Automated Microscopy Stage
          E[Cycle 1: Add TAT-Decoder Probes 1];
          E --> F[Hybridization at mRNA locations];
          F --> G[Image Acquisition (Signal 1)];
          G --> H[Photobleach or Displace Probes];
          H --> I[Cycle 2: Add TAT-Decoder Probes 2];
          I --> J[Hybridization at mRNA locations];
          J --> K[Image Acquisition (Signal 2)];
          K --> L[...]
      end
      D --> E;
      L --> M[Reconstruct 3D Temporal Transcriptome Map];
  

2.2. Continuous Industrial Bioreactor Monitoring

• Enabling Description: The technology is scaled up for monitoring a 10,000L industrial bioreactor producing monoclonal antibodies. A sidestream from the reactor flows through a detection cell with a transparent window. The detection reagents use probes targeting critical process parameters, such as host cell proteins, leaked protein A, or specific glycosylation patterns. The reagents are PNA-based for chemical robustness. The readout is automated. In each cycle, decoder probes are injected, allowed to hybridize, and detected via an integrated spectroscopic reader. The signal is removed by a high-pH wash cycle that the PNA-DNA duplex can withstand. The temporal signature identifies contaminants or product quality issues in near real-time, enabling dynamic process control.

• Mermaid Diagram:

  stateDiagram-v2
      [*] --> Idle
      Idle --> Cycle_Start: New measurement triggered
      Cycle_Start --> Hybridization: Inject Decoder Probe Set N
      Hybridization --> Detection: Flow paused, acquire signal
      Detection --> Signal_Removal: Inject high-pH wash buffer
      Signal_Removal --> Idle: If last cycle
      Signal_Removal --> Cycle_Start: If more cycles
      Detection --> Alert: If analyte level exceeds threshold
      Alert --> [*]
  

Derivative Set 3: Cross-Domain Application

3.1. Aerospace: Composite Material Fatigue Sensing

• Enabling Description: This system monitors the health of carbon fiber reinforced polymer (CFRP) structures in aircraft. The detection probes are aptamers selected to bind specifically to chemical markers of matrix degradation or delamination (e.g., specific epoxy breakdown products). These probe-reagents are infused into a sensor patch applied to a critical structural component. A portable, handheld device performs the temporal readout. It first applies a small amount of buffer to mobilize any bound reagents, then cycles through hybridization with decoder probes and imaging via a fiber-optic coupled camera. A "red-green-blue" temporal signature might indicate severe fatigue, while "green-null-red" indicates early-stage micro-cracking.

• Mermaid Diagram:

  flowchart LR
      subgraph Aircraft Wing
          A[CFRP Structure] -- contains --> B(Embedded Sensor Patch);
      end
      subgraph Handheld Reader
          C[Fluidics Module] -- injects --> B;
          D[Optics Module] -- images --> B;
          C <--> D;
      end
      subgraph Process
          E{Attach Reader to Patch};
          E --> F[Inject Decoder Set 1];
          F --> G[Image Signal 1];
          G --> H[Strip Signal];
          H --> I[Inject Decoder Set 2];
          I --> J[Image Signal 2];
          J --> K[...];
          K --> L(Analyze Temporal Signature);
          L --> M{Assess Structural Health};
      end
  

#### **3.2. AgTech: In-Field Plant Pathogen Detection**

*   **Enabling Description:** A ruggedized, battery-powered handheld device for farmers to diagnose crop diseases on-site. The farmer takes a leaf punch, places it in a disposable cartridge containing lysis buffer and a plurality of detection reagents. Each reagent's probe is a DNA oligonucleotide complementary to a specific pathogen's genomic RNA/DNA (e.g., for Tobacco Mosaic Virus, Wheat Rust Fungus). The cartridge is inserted into the device, which automates the temporal readout. Microfluidic pumps cycle decoder probes labeled with quantum dots (for high photostability in sunlight) over the sample. A CMOS sensor detects the signals. The device displays the identified pathogen and its concentration within minutes.

*   **Mermaid Diagram:**
    ```mermaid
    sequenceDiagram
        actor Farmer
        participant Cartridge
        participant Device

        Farmer->>Cartridge: Insert Leaf Sample
        Farmer->>Device: Insert Cartridge
        Device->>Cartridge: Lyse sample, release reagents
        Device->>Device: Start Temporal Readout Protocol
        loop For N cycles
            Device->>Cartridge: Pump Decoder Probes (Set N)
            Device->>Cartridge: Incubate
            Device->>Cartridge: Image with CMOS sensor
            Device->>Cartridge: Pump Wash Buffer
        end
        Device->>Device: Decode Temporal Barcodes
        Device->>Farmer: Display "Wheat Rust: High"
    ```

### **Derivative Set 4: Integration with Emerging Tech**

#### **4.1. AI-Powered Signal Deconvolution and Barcode Design**

*   **Enabling Description:** The temporal detection process is integrated with a deep learning model. A convolutional neural network (CNN) is trained to analyze the image stacks from each cycle. The AI can deconvolve signals from spatially overlapping detection reagents in dense samples, correcting for bleed-through between fluorescent channels and imperfect probe stripping. Furthermore, a generative adversarial network (GAN) is used for *in silico* design of the nucleic acid label subsequences. The generator proposes barcode sets, and the discriminator, trained on thermodynamic and kinetic simulation data, predicts their cross-hybridization potential and overall decoding error rate. The system evolves optimal barcode libraries for specific numbers of analytes and imaging conditions.

*   **Mermaid Diagram:**
    ```mermaid
    graph TD
        subgraph Barcode Design (GAN)
            A[Generator] -- Proposes Barcode Set --> B(Discriminator);
            B -- Predicts Error Rate --> A;
            B --> C{Optimal Barcode Library};
        end
        subgraph Assay Execution
            D[Sample] --> E{Temporal Imaging};
            E --> F[Raw Image Stack];
        end
        subgraph Signal Deconvolution (CNN)
            F --> G[AI Model];
            G --> H{Decoded Analytes & Locations};
        end
        C --> D;
        H --> I[Final Report];

4.2. Blockchain for Clinical Diagnostic Data Integrity

• Enabling Description: In a clinical pathology workflow, a tissue biopsy is processed using the temporal barcoding method to detect a panel of 50 cancer biomarkers. The unique temporal barcode for each of the 50 detection reagents is pre-registered. After the automated microscope completes the N cycles of readout, the raw image data is processed. For each detected analyte, the system generates a data packet containing: (1) the spatial (X,Y,Z) coordinates, (2) the detected temporal signature (e.g., R-G-B-N), and (3) a confidence score. This data packet is cryptographically hashed, and the hash is submitted as a transaction to a private, permissioned blockchain (e.g., Hyperledger Fabric). This creates an immutable, timestamped, and auditable record of the diagnostic result, ensuring data integrity from the instrument to the patient's record.

• Mermaid Diagram:

  sequenceDiagram
      participant Microscope
      participant AnalysisServer as Analysis Server
      participant Blockchain
      participant EMR as Electronic Medical Record
  
      Microscope->>AnalysisServer: Transfer Raw Image Stack
      AnalysisServer->>AnalysisServer: Decode Temporal Signatures
      AnalysisServer->>Blockchain: Submit Hash(Result Data) as Transaction
      Blockchain-->>AnalysisServer: Confirm Transaction
      AnalysisServer->>EMR: Push Verified Diagnostic Report
  

Derivative Set 5: The "Inverse" or Failure Mode

5.1. Limited-Use Reagents with Photolabile Linkers

• Enabling Description: This derivative is designed for single-use diagnostic kits or applications requiring data security. The nucleic acid label is synthesized with one or more internal photolabile linkers (e.g., a nitrobenzyl-based photocleavable group) placed between subsequences. The readout process proceeds normally for a pre-determined number of cycles. However, the final step in the protocol involves exposing the sample to a specific wavelength of UV light (e.g., 365 nm). This exposure cleaves the photolabile linkers, fragmenting the nucleic acid label and rendering it incapable of further hybridization or re-analysis. This ensures single-use compliance and prevents reverse engineering of proprietary probe sequences.

• Mermaid Diagram:

  flowchart TD
      A[Full-Length Label: Sub1-PL-Sub2-PL-Sub3] --> B{Bind to Analyte};
      B --> C{Perform N Readout Cycles};
      C --> D{Expose to 365nm UV Light};
      D --> E[Photocleavage at PL sites];
      E --> F[Fragmented Label: Sub1 + Sub2 + Sub3];
      F --> G(Assay Inactivated);
  

Combination Prior Art Scenarios

1. Combination with Micro-Manager and ImageJ: The entire temporal detection method is automated using the open-source Micro-Manager microscopy control software. A BeanShell script controls a fluidic pump (via serial port) to deliver decoder and wash buffers, triggers image acquisition using the system's camera at each time point, and calls an ImageJ/Fiji macro. The macro performs image registration to correct for sample drift between cycles and then extracts intensity values at specific ROIs, outputting a CSV file of the temporal signal signatures. This combination makes the core method obvious to implement with standard, freely available laboratory software.

2. Combination with FASTQ Data Standard: The output of a temporal barcoding experiment is converted into the standard FASTQ format used for next-generation sequencing. Each detected spatial location becomes a "read." The "sequence" line consists of characters representing the signals (e.g., 'R' for red, 'G' for green, 'N' for null). The "quality" line consists of ASCII characters encoding the signal-to-noise ratio or intensity of the signal at each corresponding cycle. This allows bioinformaticians to use existing NGS alignment tools like BWA or Bowtie to "align" the temporal codes against a reference file of known barcodes, leveraging a massive existing software ecosystem for decoding.

3. Combination with OME-Zarr Format: For large-scale spatial transcriptomics applications, the output data is stored in the OME-Zarr format, an open-source standard for large, multi-dimensional bioimaging data. The 5D image data (X, Y, Z, channel, time-point) is stored in a chunked, cloud-friendly Zarr array. The decoded temporal barcode identifying the analyte at each (X,Y,Z) coordinate is stored as an associated label mask or in an accompanying metadata table, directly linking the spatial and temporal information in a standardized, interoperable format.