Ready for your all-in-one single cell sequencing solution?

AI Virtual Cell (AIVC)

Your data generation engine for AIVC

Generate model-specific and disease-specific single cell multi-omic data at scale.

Effective AIVC models need more than big data: they need biological contexts that match the questions asked.

The data building blocks for AIVC models

Different data sources represent different biological contexts

Public datasets

Published datasets cover various cell types and tissues. However, not all demographics or diseases are equally represented.

Cell-line perturbations

Perturbation data reveal causal relationships between an intervention and the resulting changes in cell state.

Patient samples

Disease-specific data capture individual variability due to biological, environmental, or behavioral differences.

Singleron advantages

Singleron generates single cell multi-omics data for AI Virtual Cell (AIVC) models, from clinical samples and cell-line perturbation experiments. We offer 30+ multi-omic options and hands-on experience across 2,000+ sample types. We support your AIVC models with biologically relevant single cell data.

30+

single cell multi-omics products

Class II

medical device approval for automated single cell analysis

6

of the top 10 global pharmas supported

1000+

publications supported

Programs supported by Singleron's data generation

CERTAINTY

Building a virtual twin for personalized CAR-T immunotherapy in multiple myeloma.

B2B-RARE

Using AI and multi-omic data to identify treatment options for neuromuscular disorders.

AD-Omics

Building a virtual model for health management and longevity.

Resources

AIVC

AI Virtual Cell Model (AIVC)

What if we could observe how a human cell responds to a drug, a genetic change, or an environmental shift—without performing a single wet‑lab experiment?…

Read more

Let us be your data generation engine

Office-Consultation-Discuss-Talk

Let us be your data generation engine

Drop us a message, and our scientific team will come back with the sample types, assays, cohort size, and timeline we would recommend.