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Technology behind Superfluid

Key developments in molecular biology, bioinformatics and drug development have positioned Superfluid to leverage cf-mRNA to create accurate and clinically useful diagnostic tests.

  • Superfluid has developed new methods for characterizing highly complex, rare fragments of mRNA in blood
  • Evolution of knowledge around pathways and novel computational techniques for processing and interpreting big data sets has enabled the formation of the Superfluid proprietary database
  • Proliferation of expensive and highly effective targeted therapeutics has created an urgent need for diagnostics to facilitate informed selection
mRNA Polymerase Nontemplate Strand Template Stand mRNA Ribonucleotide

  • RNA Isolation

  • Quantification

    Sequencing and qPCR

  • Identification

    Tissue specific gene expression database

  • Separation

  • Algorithm development by machine learning

  • Deconvolution of disease state

References:

Koh W, et al. (2014)
Noninvasive in vivo monitoring of tissue-specific global gene expression in humans. PNAS 111(20):7361–7366.

Toden S, et al. (2020)
Noninvasive characterization of Alzheimer’s disease by circulating, cell-free messenger RNA next-generation sequencing. Sci Adv. Dec 9;6(50):eabb1654.

Vorperian S, et al. (2022)
Cell types of origin of the cell-free transcriptome. Nat Biotechnol. Jun;40(6):855-861.

Zhuang J, et al. (2022)
Survey of extracellular communication of systemic and organ-specific inflammatory responses through cell free messenger RNA profiling in mice. EBioMedicine. Sep;83:104242.

Chalasani N, et al. (2021)
Noninvasive stratification of nonalcoholic fatty liver disease by whole transcriptome cell-free mRNA characterization. Am J Physiol Gastrointest Liver Physiol. 2021 Apr 1;320(4):G439-G449

Ibarra A, et al. (2020)
Non-invasive characterization of human bone marrow stimulation and reconstitution by cell-free messenger RNA sequencing. Nat Commun. Jan 21;11(1):400.

Ngo T, et al. (2018)
Noninvasive blood tests for fetal development predict gestational age and preterm delivery. Science. Jun 8;360(6393):1133-1136.

Moufarrej M, et al. (2022)
Early prediction of preeclampsia in pregnancy with cell-free RNA. Nature. Feb;602(7898):689-694.

Chalasani N, et al. (2023)
Circulating cell-free messenger RNA secretome characterization of primary sclerosing cholangitis. Hepatol Commun. May 23;7(6):e0140.

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