D-SIFR® technology

Cryptobiotix’s SIFR® (‘cipher’) technology simulates in the laboratory the colonic fermentation, while the D-SIFR® (‘decipher’) additionally integrates digestion. They combine in vitro and ex vivo approaches to enable you to investigate more products, while considering biological variation. We then can process the generated samples through a wide array of throughput- or depth-focusing analyses to balance your project scope and budget, before integrating the data into a visually compelling report.

D-SIFR® simulation: digestion & fermentation

Increasing throughput – The SIFR technology builds on an established ex vivo fermentation strategy to address gut microbiome questions in a robust and targeted fashion. We redesigned its implementation to increase its throughput, and leveraged recent in vivo insights to increase its biorelevance, addressing the current need for more and better data. Considering interindividual variability in microbiome responses is at the core of each SIFR experiment. Together with a robust implementation, the SIFR yields precise data to support the detection of significant treatment effects across donors, instead of across technical replicates.

Miniaturisation – Each bioreactor works with a defined volume in the mL range. The volume has been optimised to maximise the number of potential downstream analyses, while allowing to work with less than a gram of your product. We implemented miniaturisation in a step-wise fashion to ensure the predictability and robustness of the SIFR technology would be maintained or even increased. Another advantage of miniaturisation is the possibility to work with premium enzymes often neglected in preclinical applications.

Decipher the gut

Our D-SIFR technology revolves around 3 core principles:

  1. Throughput and miniaturisation: reducing costs & waste
  2. Physiological relevance & robustness: reducing bias, improving precision & predictability
  3. Visual storytelling: increase interpretability of results

Physiological relevance – The SIFR technology starts from a simple yet relevant premise: increase biorelevance by reducing laboratory bias. We started by mapping the compositional ‘in vitro-in vivo bias’ of microbial simulators. The SIFR technology is being iteratively improved to minimise this key parameter, leading to unprecedented model predictability. The D-SIFR further incorporates the latest insights in in vitro digestion modelling from the INFOGEST 2.0 method (Brodkorb et al., 2019).

Robustness from early to late R&D – The same technology, a different experimental design. Adapting experimental parameters depending on your research question, the number of products to investigate and your budget allows you to either stage a screening phase before an in-depth investigation, or to create a hybrid experimental design answering a maximum of questions with the fastest turnaround time.

Sample analysis

Fermentative parametersFundamental indicators of fermentation provide a quick and efficient means to evaluate any test product.

Composition – Your focus on taxonomic depth or cost-efficiency and speed will dictate whether to use 16S rRNA gene profiling or shotgun sequencing. We can complement the relative abundance with flow cytometry quantification (Vandeputte et al., 2017) to remove any ambiguity about the compositional impact of your products. Deep shotgun sequencing can be further implemented to investigate the shifts in functional capacity at metagenomic level.

Metabolomics – Navigating the metabolomic landscape can be time-consuming and finding the appropriate depth of analysis and interpretation even more frustrating. We provide three different MS-based techniques to balance analytical depth and budget, addressing both your targeted and untargeted needs.

Specific analyses? – Whether you are interested in amino acids, minerals, carbohydrates, peptides or any other compounds, reach out to us to determine the best analytical path forward. Go further than the gut microbiome and also investiagate potential host responses through cell lines and primary cell models.


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