A multidimensional single-cell application platform powered by Live-Cell Imaging & AI.
We help biologists better leverage bioimaging data and build faster
We use structured datasets to build disease models that are representative of cellular behavior in a controlled environment. This helps researchers better understand how diseases develop and quickly test potential treatments.
Biologists spend a lot of time & energy implementing complicated assay protocols. Automate all of your assays and focus on optimizing your bio processes
Cells are complex and narrow viewing population averaging assays don’t provide enough information. Get Single-cell data for incredible detail on all cellular activities
Cellular immunotherapies is a long and expensive process but it is a critical approach to fight a wide range of human diseases, including cancer. Assays like MTT or XTT depend on correlations with cell metabolites which makes them an indirect form of assessment and they can be in consistent when tested with different substances. By implementing time lapse microscopy and single cell biology techniques, researchers can record and monitor all cellular activity throughout the cell lifecycle in realtime. The iLABS platform is designed for detailed analysis of adherent cells with a focus on quantitative drug dose response applications.
Cellular organisms are complex and organized and connecting all of the data during an experiment or bioprocessing can be complicated. Because standard assessment techniques don’t provide enough information, we need a new approach for an in-depth observation of the complexity and dynamics of cell behavior under different environmental and physiological conditions. The iLABS platform is programmed to collect data from seeding and throughout the lifecycle of the experiment. This creates batches of rich datasets that provide unique insights into the evolution and physiology of your cell lines including mapping to tissue functions.
We are a tight knit group of biologists and engineers
dedicated to the joyful
journey of scientific
discovery. This project started out as an attempt to solve our own bioengineering problems and
then
we realized it could be very useful for other people.
Collectively, we like to think of ourselves as
citizen scientists and our core code of conduct are (1) build for good
(2) collaboration drives innovation
(3) safety should always be priority.
Something very interesting is happening within a subset of the biotech industry -
there’s a new
convergence between bioimaging and AI and it is very exciting!
Biotech used to suffer from lack of data but that is slowly changing; with new dna sequencing
and
advanced imaging technologies, more data is been generated and the new challenge is to design an
intuitive system that synthesis of all of these information and generate value for biologists.
At the moment, the experimental process is slow because there’s no good way to effectively
leverage
biodata. Our goal is to bridge the gap between bioimaging data and AI by building a modern data
management software for the biotech industry.
