Transformational method for retrieval of single cell data that breaks from 60 years of brute force dogma.
It is standard practice to analyze molecules (proteins, DNA) with molecules (antibodies, enzymes). This is incredibly efficient. Yet when it comes to cells, the standard practice is to use large instruments that sort through cells one at a time. PHENO-IMMUNO COMPUTING (PIC) is the first method that uses cells to analyze cells.
Instrument free technology for deep single cell analysis.
PIC is an information technology that is grown. And more than that, it is grown using the most widely used industrial organism, baker’s yeast. This enables unparalleled diagnostic power with incredible cost effectivity.
The method primarily comprises two steps. First, in a cell suspension, antigens of interest marking the cell membrane are labeled with commercially developed antibodies. This is a standard procedure. Second, the marked antigens are bound by genetically engineered microbes that are simply mixed into the reaction. The rest happens automatically. Attached microbes proceed to evaluate immuno profiles of their bound cells. If the immuno profile of the bound cell is different from a predetermined target profile, the microbes remain passive. However, if the immuno profile of the bound cell matches the target profile, the microbes are triggered and become active secreting various reporter molecules.
The method is completely modular. By mix and matching antibodies and microbes the user dictates the target profile. Target profiles represented by general logical gates are possible. For instance, a two-input AND gate profile requires two specific antigens to be present on the bound cell to trigger a positive readout.
This method is validated on various cell types including cancer cells, skin cells, stem cells, blood cells, etc.
Blood, urine, saliva, tissue, and other sample forms available in liquid formats.
Biomarkers of interest are defined using effective immunolabels.
Immunolabels are associated with living microbial computational units.
Molecular profiles are processed and presence of target cells is indicated.
Machine supervised learning of single cell biomarkers.
The TUBE is an automated operating system that deploys PIC for the purpose of learning highly specific single cell biomarkers. By the distributed power of biology, the TUBE is able to screen through an unprecedented number of cells in record time.
Biomarkers are a powerful decision-making tool. In clinical trials, biomarker based patient stratification is becoming the standard tool for improving drug efficacy and clinical trial outcomes. In the single cell space, however, biomarkers are almost non-existent.
Much like a computer retrieves bits of data stored on GB or TB memory drives, the TUBE looks through billions or trillions of cells and biologically retrieves molecular data from pertinent cellular entities.
A diverse set of samples is uploaded to the TUBE. Proprietary preservation enables high-throughput analysis by PIC.
Depth of single cell knowledge is greatly increased with unsupervised learning of many more patients and many more cells.
Single cell biomarker discovery is today made infeasible by the bottleneck of phenotypic analysis.
There exists no facility today that can perform meaningful phenome-wide analysis of semi-rare diseases like leukemia in a reasonable amount of time. A 100K sample study performed in a reasonable amount of time would require more instruments than companies sell in a year (see below, figure on the right). And while 100K samples is a lot, chemical analysis of the same lot is easily accomplished with routine laboratory instruments. The problem is that the current approach dedicates 99.999% of the resources to process material that is informationally uninteresting. PIC provides a biological solution that works in a distributed way extracting only essential information, much like our own immune system. Biological analysis has an unparalleled potential to scale (see below, figure on the left).
An elephant is healthier than a mouse despite having many more cells it must maintain. This is known as Peto’s paradox, and it speaks to the potential of biological systems to scale up information processing.
To validate early biomarkers of rare diseases, large studies are necessary. This is the challenge of preventive healthcare. Even the most effective instruments fall short. Costs of single cell biomarker discovery facilities are plotted above.
