A precision fragmenting instrument generating live tumor fragments with intact native tumor architecture and immune compartment.
High-speed sorter of tumor fragments into well plates, for downstream imaging and analysis.
An advanced three dimensional (3D) and label-free imaging platform using multiphoton microscopy to assess cell viability and metabolic activity in response to treatment.
A dynamic visualization platform for analyzing 3D multiphoton images, multi-omic assays, secreted factors, and cell markers. All data and results delivered securely via the cloud.
Cybrid™ incorporates a comprehensive suite of assays to help understand the mechanisms of action of immunotherapeutic compounds indicating drug response.
Measurement of cellular subsets and activation markers
Profiling of innate and adaptive immune response using secreted factors.
Detection of immune response at the mRNA level
Tumor biopsy or resection
Tumor fragmentation
Fragment sorting & dispensing
Detect live tumor fragments & monitor drug response
Predict response & data visualization
LTFs derived from tissue biopsies, retain the native tumor architecture, including all the immune cells which are required to predict response to immunotherapy.
Unlike spheroids or organoids, there is no expansion of cells or autologous infusion of immune cells, giving a faster turnaround time to test how effectively a drug works.
Additionally, LTFs allow us to look at immune cell infiltration which is predictive of response to therapy.
Conventional techniques such as confocal microscopy can be destructive and has limited penetration into the tissue sample.
Elephas’ multiphoton imaging is non-destructive, enables real-time monitoring of immune cells, and allows label-free imaging at significantly higher penetration depths to look at living tumors in 3D.
Multiplexed assays are routinely used to profile cell markers, cytokines, chemokines and mRNA expression to determine response to drugs.
By combining multiphoton imaging, we can analyze spatial and temporal information to visualize in the impact of drugs on cellular structures over time.
The combination of all these techniques also generates a vast amount of data that can be used to train AI algorithms to predict response to different therapies.