Computational platform and app for quantifying band intensities from diagnostic lateral flow strips. The code presented in this repo accompany the following manuscript:
Michael M. Kaminski, Miguel A. Alcantar, Isadora Lape, Robert Greensmith, Allison C. Huske,Jacqueline A. Valeri, Francisco M. Marty, Verena Klämbt, Jamil Azzi, Enver Akalin, Leonardo V. Riella, and James J. Collins. Towards CRISPR-based diagnostics for transplantation medicine. In review
Repo was compiled by: Jacqueline A. Valeri and Miguel A. Alcantar.
Infection and rejection are major causes of graft loss in organ transplantation and are linked by the net state of immunosuppression. Refined strategies to monitor transplanted patients are needed to diagnose and treat these conditions earlier in order to improve long-term outcomes. Here, we show that CRISPR-Cas13 enables fast, low-cost, sensitive point-of-care detection of BK polyomavirus DNA, cytomegalovirus DNA and CXCL9 mRNA, allowing efficient monitoring of common opportunistic viral infections and rejection post-transplantation. BK virus and cytomegalovirus were detected from patient-derived blood and urine samples with high sensitivity and specificity. Similarly, CXCL9 mRNA was detected at elevated levels in urine samples from patients experiencing acute renal transplant rejection. The assay was also adapted for lateral flow read-out, enabling simple visualization and interpretability of results. This work demonstrates the potential for CRISPR-Cas13 diagnostics to facilitate point-of-care post-transplantation monitoring.
lat_flow_app/: self-contained source code for the Android Appocr/: auxillary functions for image processingsample_images/: sample lateral flow assay images to run with the app or jupyter notebookLateral_flow_quantifier.ipynb: jupyter notebook with signal quantification workflow (using openCV)LICENSE.txt: GPLv3 Licensedemo_positive_samples.mov: Supplementary Video File 1 showcasing the app
git clone https://github.com/jackievaleri/lateral_flow_quantification_app.git
Android app code can be compiled using Android Studio (Google, Mountain View, CA). The jupyter notebook demonstrates the workflow used to process lateral flow images and can be used to process sample images.
To utilize the code, download the Jupyter notebook and sample_images folder and run. To utilize and build off the app, clone the repository (see installation) and build a new project with Android Studio or another android IDE. We are currently working on getting an open source license from Chaquopy so there is a time limit on the app at the moment.
To reference the app's capability, please see the demo video included with the paper and in this repo: demo_positive_samples.mov