In Aging, researchers from Spain and Luxembourg have described the creation of Single-cell RNA-seq Investigation of Rejuvenation Agents and Longevity (SINGULAR), an atlas for cellular rejuvenation that describes how interventions affect individual cells.
Computational biology might light up a better path
These researchers begin this paper by describing the problems with current interventions against the processes of aging. They hold that parabiosis is not feasible for human beings, partial reprogramming to bring cells back to a youthful state is still too dangerous to carry out in the clinic, and caloric restriction and exercise regimes, despite their vaunted effectiveness, are not things that people normally comply with for extended periods of time.
Additionally, they note that there is no transcriptomic standard for assessing the impact of interventions. Previous papers on parabiosis [1], caloric restriction [2], and exercise [3] have all investigated what is happening to the cellular transcriptome under those conditions, but these papers did not all select their cells in the same way.
However, these researchers also note that strides in computation, specifically computational network biology, have allowed for a more thorough understanding of the transcriptome along with intercellular communication, which allows the research community to better create and test hypotheses [4]. Therefore, they have created SINGULAR as a unified framework for analyzing cells, defining aging as a “metastable transcriptional state associated with loss of regular physiological function”. Rejuvenation, therefore, is accomplished by any intervention that reverses this loss.
A broad effort to restore function
This team used SINGULAR to analyze nine studies on six interventions (parabiosis, caloric restriction, exercise, metformin, rapamycin, and partial reprogramming), work that encompassed 74 distinct cell types in 18 organs, even though these studies were conducted at different levels of sequencing depth. In this process, low quality cells were filtered out, changes relating to cell cycle were normalized [5], and cells were clustered using an artificial intelligence algorithm. Further algorithms analyzed intercellular communication [6] and signaling molecules [7].
This initial examination found some surprising effects. The effects of these interventions were strongly heterogenous, although metformin was found to have few effects on any organs. While exercise diverts blood to the lungs, the strongest effects of exercise were found in liver, arterial, and spinal cord cells.
The researchers further found that there are two main ways that rejuvenation interventions affect gene expression: through transcriptional regulators (TRNs), of which the researchers found 317, along with signaling molecules. While these TRNs appeared to be hierarchical in nature, with a few of them able to affect many different genes, the heterogenous cells did not share these master TRNs. Additionally, the effects of some of these regulators only appeared in specific interventions.
While three of the four Yamanaka partial reprogramming factors were rediscovered in this process, this analysis found little overlap with previous work linking gene expression to natural aging [8]. The researchers, therefore, believe that the genes associated with aging and the ones associated with rejuvenation phenotypes have little to do with each other. Additionally, many of these genes have been found in previous work to have many other effects, such as proliferation and differentiation.
The researchers found signaling cascades that had not been previously documented in heterochronic parabiosis and in exercise, finding that parabiosis upregulates macrophage responsiveness (in addition to neutrophil inflammation) and that exercise’s downstream effects appear to upregulate a known factor in neuroprotection.
Only 17 of the master regulators, however, were considered to be druggable targets according to the DrugBank database. Cross-referencing them with the DrugAge database, which documents drugs known to have rejuvenative effects in model organisms [9], revealed that some of these drugs also have effects on the genes identified by SINGULAR.
This work is, of course, preliminary, and it may be that more interventions can be found to restore cellular function according to SINGULAR’s metrics. Whether or not this will translate into real rejuvenation for living organisms, including human beings, will require significant amounts of drug discovery and clinical work to determine.
Literature
[1] Ma, S., Wang, S., Ye, Y., Ren, J., Chen, R., Li, W., … & Liu, G. H. (2022). Heterochronic parabiosis induces stem cell revitalization and systemic rejuvenation across aged tissues. Cell Stem Cell, 29(6), 990-1005.
[2] Ma, S., Sun, S., Geng, L., Song, M., Wang, W., Ye, Y., … & Liu, G. H. (2020). Caloric restriction reprograms the single-cell transcriptional landscape of Rattus norvegicus aging. Cell, 180(5), 984-1001.
[3] Sun, S., Ma, S., Cai, Y., Wang, S., Ren, J., Yang, Y., … & Liu, G. H. (2023). A single-cell transcriptomic atlas of exercise-induced anti-inflammatory and geroprotective effects across the body. The Innovation, 4(1).
[4] Del Sol, A., & Jung, S. (2021). The importance of computational modeling in stem cell research. Trends in Biotechnology, 39(2), 126-136.
[5] Hafemeister, C., & Satija, R. (2019). Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome biology, 20(1), 296.
[6] Gonçalves, C. A., Larsen, M., Jung, S., Stratmann, J., Nakamura, A., Leuschner, M., … & Grapin-Botton, A. (2021). A 3D system to model human pancreas development and its reference single-cell transcriptome atlas identify signaling pathways required for progenitor expansion. Nature communications, 12(1), 3144.
[7] Ravichandran, S., Hartmann, A., & Del Sol, A. (2020). SigHotSpotter: scRNA-seq-based computational tool to control cell subpopulation phenotypes for cellular rejuvenation strategies.
[8] Maity, A. K., Hu, X., Zhu, T., & Teschendorff, A. E. (2022). Inference of age-associated transcription factor regulatory activity changes in single cells. Nature Aging, 2(6), 548-561.
[9] Barardo, D., Thornton, D., Thoppil, H., Walsh, M., Sharifi, S., Ferreira, S., … & de Magalhães, J. P. (2017). The DrugAge database of aging‐related drugs. Aging cell, 16(3), 594-597.
