NewLimit was founded to significantly extend human healthspan. We are developing epigentic reprogramming medicines to treat age-related diseases.

Our science draws upon remarkable work done by the broader biotechnology community. Below, we outline some of the prior research that inspires our approach.

Epigenetic reprogramming to address age-related disease

Researchers have reported that epigenetic reprogramming can reduce the burden of age-related disease across diverse pre-clinical models. These results are still early, and we view them as hints that reprogramming can provide benefit in each context, rather than definitive evidence. There is still a large amount of discovery science to be done in each indication.

Disease Model Intervention Reference
Type I diabetes Partial OSKM, transgenic https://pubmed.ncbi.nlm.nih.gov/27984723/
Skeletal muscle injury Partial OSKM, transgenic https://pubmed.ncbi.nlm.nih.gov/27984723/
Skeletal muscle injury OSKMLN, mRNA + LNPs https://pubmed.ncbi.nlm.nih.gov/32210226/
Skeletal muscle injury OSKM, transgenic https://pubmed.ncbi.nlm.nih.gov/34035273/
Glaucoma Partial OSK, AAV https://pubmed.ncbi.nlm.nih.gov/33268865/
Myocardial infarction Partial OSKM, transgenic https://pubmed.ncbi.nlm.nih.gov/34554778/
Skin wound healing Partial OSKM, transgenic https://pubmed.ncbi.nlm.nih.gov/29761584/
Skin wound healing Partial OSKM, long-term, transgenic https://www.nature.com/articles/s43587-022-00183-2#Sec28
Liver regeneration Partial OSKM, transgenic https://pubmed.ncbi.nlm.nih.gov/35476977/

Pooled screening to search through large, combinatorial hypothesis spaces

Traditionally, reprogramming hypotheses have been tested in low throughput, using low-dimensional read-outs of cell state. Single cell and functional genomics technologies have unlocked a higher throughput approach.

NewLimit’s discovery engine is built upon the shoulders of these technological innovations, allowing us to run experiments at a scale that is orders of magnitude larger than traditional approaches.

Technology Description References
Single cell combinatorial indexing Massive throughput single cell genomics using next-generation library chemistries https://www.nature.com/articles/s41586-019-0969-x
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8678206/#:~:text=Single-cell combinatorial indexing (sci,of%20usable%20reads%20per%20cell.
Perturb-ATAC, CRISPR sciATAC Pooled screening of genetic perturbations using single cell ATAC-seq read-outs https://pubmed.ncbi.nlm.nih.gov/30580963/https://pubmed.ncbi.nlm.nih.gov/32231336/https://www.nature.com/articles/s41587-021-00902-x
CRISP/CROP/Perturb-seq Pooled screening of genetic perturbations using single cell RNA-seq read-outs https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5181115/pdf/nihms835459.pdf
https://pubmed.ncbi.nlm.nih.gov/27984734/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5334791/
https://pubmed.ncbi.nlm.nih.gov/32231336/
Partial-seq Pooled screening of partial reprogramming interventions with single cell genomics read-outs https://www.sciencedirect.com/science/article/pii/S240547122200223X

Machine learning models enable in silico experiments

Machine learning models now enable us to learn the rules of biological systems from data. We can use these models to perform “in silico experiments” — predicting the outcome of an experiment that has not yet occurred using the model’s learned representation of biology.

These tools allow us to run experiments in the world of bits so that we can prioritize the experiments we run in the world of atoms. We’ve been inspired by performance of in silico experiments on diverse biological tasks related to our key questions.

Task Reference
Predict diverse cell structures from a simple brightfield image https://www.nature.com/articles/s41592-018-0111-2
Predict sequence function and mutation effects from DNA sequence alone https://www.nature.com/articles/s41592-021-01252-x
Predict the effect of gene knockdowns on gene expression https://www.science.org/doi/abs/10.1126/science.aax4438
Predict chromatin accessibility from mRNA expression and vice-versa https://www.pnas.org/doi/10.1073/pnas.2023070118

Immunology

We are working to restore function in aged T cells as one of the earliest applications of our technology. T cell aging and dysfunction is linked to a variety of pathologies and limits the performance of cell therapies. Emerging evidence suggests that there is an epigenetic basis for the dysfunction that arises with both age and disease, and that this dysfunction can be reversed by reprogramming.

Description Reference
T cells enter a common dysfunctional state with age https://pubmed.ncbi.nlm.nih.gov/33271118/
T cell exhaustion is a pathological cell state with an epigenetic basis https://pubmed.ncbi.nlm.nih.gov/27789799/
Reprogramming can restore function in dysfunctional T cells https://pubmed.ncbi.nlm.nih.gov/23290140/
https://www.nature.com/articles/s41590-021-00964-8