CRISPR-Cas-amplified urinary biomarkers for multiplexed and portable cancer diagnostics

Synthetic biomarkers, bioengineered sensors that generate molecular reporters in diseased microenvironments, represent an emerging paradigm in precision diagnostics. Despite the utility of DNA barcodes as a multiplexing tool, their susceptibility to nucleases in vivo has limited their utility. Here we exploit chemically stabilized nucleic acids to multiplex synthetic biomarkers and produce diagnostic signals in biofluids that can be ‘read out’ via CRISPR nucleases. The strategy relies on microenvironmental endopeptidase to trigger the release of nucleic acid barcodes and polymerase-amplification-free, CRISPR-Cas-mediated barcode detection in unprocessed urine. Our data suggest that DNA-encoded nanosensors can non-invasively detect and differentiate disease states in transplanted and autochthonous murine cancer models. We also demonstrate that CRISPR-Cas amplification can be harnessed to convert the readout to a point-of-care paper diagnostic tool. Finally, we employ a microfluidic platform for densely multiplexed, CRISPR-mediated DNA barcode readout that can potentially evaluate complex human diseases rapidly and guide therapeutic decisions.

For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.
n/a Confirmed The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.
For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection Living Image (Version 4.5.5) was used to collect data on the IVIS® Spectrum in vivo imaging system (PerkinElmer Inc.). ImageStudio (Version 5.2) was used for the Odyssey CLx imaging system (Li-Cor Inc.). Tecan icontrol software (Version 3.7.3.0) was used to collect data on the Tecan Infinite 200pro microplate reader (Tecan Group Ltd.). Bio-Rad CFX manager 3.1 was used to collect data on the CFX96 Real Time System C1000 Thermal Cycler (Bio-Rad Laboratories Inc.). Panoramic Scanner (Version 1.22) was used to collect data on a 3DHistech P250 High Capacity Slide Scanner (PerkinElmer Inc.). ChemStation for LC 3D (Rev. B.03.01) was used to control the Agilent Model 1100 HPLC system (Agilent Technologies Inc.). UNICORN (Version 5.3.1) was used for FPLC on a AKTApurifier 10 (GE Healthcare Inc.). SoftMax Pro Version 4.8 was used for the SpectraMax Plus 384 Microplate Spectrophotometer (Molecular Devices LLC). Details are specified in Methods, Figure  Legends and Supplementary Information.

Data analysis
Living Image (Version 4.7.3, Perkin Elmer Inc.) was used for IVIS in vivo optical imaging analysis. ImageStudio (Version 5.2) was used for analysis of near-infrared images collected on the Odyssey CLx imaging system (Li-Cor Inc.). CaseViewer (Version 2.2) was used for processing of images collected on the 3DHistech P250 High Capacity Slide Scanner (3DHISTECH Ltd.). ImageJ (1.49v) was used for lateral flow strip quantification. Differential expression analyses were carried out by DESeq2 1.10.1. GraphPad Prism (Version 9) was used for data analysis and statistics. Microfluidics readout on the Fluidigm BioMark HD System was analyzed using Python 3 custom analysis codes available on GitHub: https://github.com/broadinstitute/mcarmen. Python (version 3.9) package for enzyme kinetics analysis is available through GitHub: https:// github.com/nharzallah/LMRT-NNanotech-2023. Details are specified in Methods, Figure Legends  For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.

March 2021
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy The Cancer Genome Atlas (http://cancergenome.nih.gov) and Matrisome (http://matrisomeproject.mit.edu/) are open access resources. The datasets and codes analysed during the current study are available in the Zenodo repository (https://zenodo.org/deposit/7686811). All data that support the findings of this study are available within the Article and Supplementary Information or from the corresponding author upon reasonable request.

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Life sciences study design
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Sample size
Sample sizes are provided in the figure legend for each experiment and were determined to be adequate based on the magnitude and consistency of measurable differences between groups. Samples were allowed to have at least three biological replicates to derive statistical significance. We used a sample size of minimum three mice per group for in vivo studies. Animal group sizes were also practically associated with the number of mice housed per cage (n=5 mice per cage). When comparison between two animal groups were conducted (e.g. comparing urinary barcode levels), sample size are selected to ensure that the number in one animal group is greater than or equal to five at each time point and for each treatment group, because a group size of four can provide 80% power to detect a 1.5-fold difference in urinary barcode levels (two-sided t-test, α set at 0.05). Numbers of animals per group were specified in the figure legends.
Data exclusions No data was excluded for in vitro experiments. Pre-established exclusion criteria were set based on failed injection or urine production within defined time course of the animal study. Animals were excluded solely on the basis of the pre-established exclusion criteria.

Replication
All experiments were repeated independently at least twice with similar results. Experiments were repeated and visualized by two independent researchers when results from representative experiments (such as histological or fluorescent micrographs) are shown.
Randomization In vitro samples were prepared, processed, and analyzed in a random order, for instance, DNA barcodes were randomly numbered. Cultured cells were randomly assigned to experimental groups. All animals analyzed in this study were sex-and age-matched. Littermates of the same sex were randomly assigned to experimental and control groups. Grouping criteria of animals was included in Methods and figure legends.

Blinding
For histological evaluation of toxicity, investigators were not blinded to group allocation during data collection but tissue sections were analyzed by a veterinary pathologist who was blinded to the treatment groups. In all other cases, investigators were not blinded to the groups and treatments during experiments, because the investigators who set up the experiments carried out the analyses. Data reported for these experiments are not subjective but based on the quantitative assays described in Methods and figure legends.

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