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 Ocampo 2016
Skeletal muscle injury Partial OSKM, transgenic Ocampo 2016
Skeletal muscle injury OSKMLN, mRNA + LNPs Sarkar 2020
Skeletal muscle injury OSKM, transgenic Wang 2021
Glaucoma Partial OSK, AAV Lu 2020
Myocardial infarction Partial OSKM, transgenic Chen 2021
Skin wound healing Partial OSKM, transgenic Doeser 2018
Skin wound healing Partial OSKM, long-term, transgenic Browder 2022
Liver regeneration Partial OSKM, transgenic Hishida 2022

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 Cao 2019
[Mulqueen 2022](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8678206/#:~:text=Single-cell combinatorial indexing (sci,of usable reads per cell.)
Perturb-ATAC, CRISPR sciATAC Pooled screening of genetic perturbations using single cell ATAC-seq read-outs Rubin 2019
Liscovitch-Brauer 2021
CRISP/CROP/Perturb-seq Pooled screening of genetic perturbations using single cell RNA-seq read-outs Dixit 2016
Jaitin 2016
Datlinger 2017
Replogle 2020
Partial-seq Pooled screening of partial reprogramming interventions with single cell genomics read-outs Roux 2022

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 sequence function and mutation effects from DNA sequence alone Avsec 2021
Predict the effect of gene knockdowns on gene expression Norman 2019
Predict perturbation outcomes from gene representations Roohani 2024
Iterative experimental design accelerates the discovery of genetic interve Huang 2023

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 Mogilenko 2021
T cell exhaustion is a pathological cell state with an epigenetic basis Sen 2016
Reprogramming can restore function in dysfunctional T cells Nishimura 2013
Seo 2021

Metabolism

Our Metabolism program is focused on restoring youthful function in aged hepatocytes. Initially, we hope to treat chronic liver diseases that represent exaggerated forms of the age-related degeneration we all experience. In the long term, we believe medicines that restore youthful hepatic function may treat features of metabolic syndrome (obesity, hypertension, type 2 diabetes) that develop in many aging adults.

Description Reference
Aged hepatocytes are less regenerative & competent than young hepatocytes Wang 2001
Kubota 2018
In vivo genetic screening reveals regenerative interventions Jia 2022
Wang 2023
Zwirner 2024
Reprogramming can restore function in aged & diseased hepatocytes Yang 2021
Hishida 2022