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 |