CRISPR-based editing reveals edge-specific effects in biological networks

Li, Yang; Nowak, Chance M.; Withers, Daniel; Pertsemlidis, Alexander; Bleris, Leonidas

doi:10.1101/265710

Cited by 3 publications

(3 citation statements)

References 42 publications

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“…The human colorectal cancer cell line HCT116 was reverse transfected with miR-34a-5p mimic (final concentration: 25 nM) 36 . The cells were then stained with Annexin V-Alexa Fluor 488 dye and PI before being analyzed by flow cytometry 37 .…”

Section: Resultsmentioning

confidence: 99%

Cell morphology-based machine learning models for human cell state classification

Nowak

Pham

et al. 2021

npj Syst Biol Appl

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Herein, we implement and access machine learning architectures to ascertain models that differentiate healthy from apoptotic cells using exclusively forward (FSC) and side (SSC) scatter flow cytometry information. To generate training data, colorectal cancer HCT116 cells were subjected to miR-34a treatment and then classified using a conventional Annexin V/propidium iodide (PI)-staining assay. The apoptotic cells were defined as Annexin V-positive cells, which include early and late apoptotic cells, necrotic cells, as well as other dying or dead cells. In addition to fluorescent signal, we collected cell size and granularity information from the FSC and SSC parameters. Both parameters are subdivided into area, height, and width, thus providing a total of six numerical features that informed and trained our models. A collection of logistical regression, random forest, k-nearest neighbor, multilayer perceptron, and support vector machine was trained and tested for classification performance in predicting cell states using only the six aforementioned numerical features. Out of 1046 candidate models, a multilayer perceptron was chosen with 0.91 live precision, 0.93 live recall, 0.92 live f value and 0.97 live area under the ROC curve when applied on standardized data. We discuss and highlight differences in classifier performance and compare the results to the standard practice of forward and side scatter gating, typically performed to select cells based on size and/or complexity. We demonstrate that our model, a ready-to-use module for any flow cytometry-based analysis, can provide automated, reliable, and stain-free classification of healthy and apoptotic cells using exclusively size and granularity information.

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Section: Resultsmentioning

confidence: 99%

Cell morphology-based machine learning models for human cell state classification

Nowak

Pham

et al. 2021

npj Syst Biol Appl

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“…The development of CRISPR technology has literally transfigured the capability of genome editing and has opened unique scenarios to analyze pathways and biological networks at the level of single‐nucleotide specificity (Jinek et al, 2012). To demonstrate the impact that new molecular technologies can exert on network biology and Network Medicine, Li, Nowak, Withers, Pertsemlidis, and Bleris (2018) used CRISPR for outlining the role of network edges compared to that of nodes; they targeted a number of miRNA sites in 71 genes involved in the p53 pathway, ablating numerous edges and thus demonstrating their essentiality for the pathway function and stability. From this perspective, CRISPR editing in biological network analysis seems to provide a finer resolution tool compared to more disruptive node removal techniques, potentially leading to the identification of previously hidden interactions and more opportunities for therapeutic intervention.…”

Section: Conclusion: Knowledge Gaps and Key Research Directionsmentioning

confidence: 99%

Molecular networks in Network Medicine: Development and applications

Silverman

Schmidt

Anastasiadou

et al. 2020

WIREs Mechanisms of Disease

156

118

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Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases.

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“…To validate our hypothesis, and towards implementing the first generation of genetic PUFs, we carried out a pilot study where we leverage genome engineering using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) 27–30 . CRISPR is an immune response mechanism against bacteriophage infections in bacteria and archaea that has revolutionized the field of genome editing and spurred myriads of applications critically relevant to agriculture, biomanufacturing, and human health 28,31–35 .…”

Section: Resultsmentioning

confidence: 99%

Provenance Attestation of Human Cells Using Physical Unclonable Functions

Kang

et al. 2021

Preprint

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We introduce a novel methodology, namely CRISPR-Engineered Attestation of Mammalian Cells using Physical Unclonable Functions (CREAM-PUFs), which can serve as the cornerstone for formally verifying transactions in human cell line distribution networks. A PUF is a physical entity which provides a measurable output that can be used as a unique and irreproducible identifier for the artifact wherein it is embedded. Popularized by the electronics industry, silicon PUFs leverage the inherent physical variations of semiconductor manufacturing to establish intrinsic security primitives for attesting integrated circuits. Owing to the stochastic nature of these variations and the multitude of steps involved, photo-lithographically manufactured silicon PUFs are impossible to reproduce (thus unclonable). Inspired by the success of silicon PUFs, we sought to exploit a combination of sequence-restricted barcodes and the inherent stochasticity of CRISPR-induced non-homologous end joining DNA error repair to create the first generation of genetic physical unclonable functions in three distinct human cells (HEK293, HCT116, and HeLa). We demonstrate that these CREAM-PUFs are robust (i.e., they repeatedly produce the same output), unique (i.e., they do not coincide with any other identically produced PUF), and unclonable (i.e., they are virtually impossible to replicate). Accordingly, CREAM-PUFs can serve as a foundational principle for establishing provenance attestation protocols for protecting intellectual property and confirming authenticity of engineered cell lines.

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CRISPR-based editing reveals edge-specific effects in biological networks

Cited by 3 publications

References 42 publications

Cell morphology-based machine learning models for human cell state classification

Cell morphology-based machine learning models for human cell state classification

Molecular networks in Network Medicine: Development and applications

Provenance Attestation of Human Cells Using Physical Unclonable Functions

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