Lack of evidence for increased transcriptional noise in aged tissues

A high-resolution view of the heterogeneous aging endothelium

Vascular endothelial cell (EC) aging has a strong impact on tissue perfusion and overall cardiovascular health. While studies confined to the investigation of aging-associated vascular readouts in one or a few tissues have already drastically expanded our understanding of EC aging, single-cell omics and other high-resolution profiling technologies have started to illuminate the intricate molecular changes underlying endothelial aging across diverse tissues and vascular beds at scale. In this review, we provide an overview of recent insights into the heterogeneous adaptations of the aging vascular endothelium. We address critical questions regarding tissue-specific and universal responses of the endothelium to the aging process, EC turnover dynamics throughout lifespan, and the differential susceptibility of ECs to acquiring aging-associated traits. In doing so, we underscore the transformative potential of single-cell approaches in advancing our comprehension of endothelial aging, essential to foster the development of future innovative therapeutic strategies for aging-associated vascular conditions.

A neural network-based model framework for cell-fate decisions and development

Gene regulatory networks (GRNs) fulfill the essential function of maintaining the stability of cellular differentiation states by sustaining lineage-specific gene expression, while driving the progression of development. However, accounting for the relative stability of intermediate differentiation stages and their divergent trajectories remains a major challenge for models of developmental biology. Here, we develop an empirical data-based associative GRN model (AGRN) in which regulatory networks store multilineage stage-specific gene expression profiles as associative memory patterns. These networks are capable of responding to multiple instructive signals and, depending on signal timing and identity, can dynamically drive the differentiation of multipotent cells toward different cell state attractors. The AGRN dynamics can thus generate diverse lineage-committed cell populations in a robust yet flexible manner, providing an attractor-based explanation for signal-driven cell fate decisions during differentiation and offering a readily generalizable modelling tool that can be applied to a wide variety of cell specification systems.

Translation is an emerging constraint on protein homeostasis in ageing

Proteins are molecular machines that provide structure and perform vital transport, signalling and enzymatic roles. Proteins expressed by cells require tight regulation of their concentration, folding, localisation, and modifications; however, this state of protein homeostasis is continuously perturbed by tissue-level stresses. While cells in healthy tissues are able to buffer against these perturbations, for example, by expression of chaperone proteins, protein homeostasis is lost in ageing, and can lead to protein aggregation characteristic of protein folding diseases. Here, we review reports of a progressive disconnect between transcriptomic and proteomic regulation during cellular ageing. We discuss how age-associated changes to cellular responses to specific stressors in the tissue microenvironment are exacerbated by loss of ribosomal proteins, ribosomal pausing, and mistranslation.

Cellular age explains variation in age-related cell-to-cell transcriptome variability

Organs and tissues age at different rates within a single individual. Such asynchrony in aging has been widely observed at multiple levels, from functional hallmarks, such as anatomical structures and physiological processes, to molecular endophenotypes, such as the transcriptome and metabolome. However, we lack a conceptual framework to understand why some components age faster than others. Just as demographic models explain why aging evolves, here we test the hypothesis that demographic differences among cell types, determined by cell-specific differences in turnover rate, can explain why the transcriptome shows signs of aging in some cell types but not others. Through analysis of mouse single-cell transcriptome data across diverse tissues and ages, we find that cellular age explains a large proportion of the variation in the age-related increase in transcriptome variance. We further show that long-lived cells are characterized by relatively high expression of genes associated with proteostasis and that the transcriptome of long-lived cells shows greater evolutionary constraint than short-lived cells. In contrast, in short-lived cell types, the transcriptome is enriched for genes associated with DNA repair. Based on these observations, we develop a novel heuristic model that explains how and why aging rates differ among cell types.

Transcriptional maintenance of cortical somatostatin interneuron subtype identity during migration

Although cardinal cortical interneuron identity is established upon cell-cycle exit, it remains unclear whether specific interneuron subtypes are pre-established, and if so, how their identity is maintained prior to circuit integration. We conditionally removed Sox6 (Sox6-cKO) in migrating somatostatin (Sst+) interneurons and assessed the effects on their mature identity. In adolescent mice, five of eight molecular Sst+ subtypes were nearly absent in the Sox6-cKO cortex without a reduction in cell number. Sox6-cKO cells displayed electrophysiological maturity and expressed genes enriched within the broad class of Sst+ interneurons. Furthermore, we could infer subtype identity prior to cortical integration (embryonic day 18.5), suggesting that the loss in subtype was due to disrupted subtype maintenance. Conversely, Sox6 removal at postnatal day 7 did not disrupt marker expression in the mature cortex. Therefore, Sox6 is necessary during migration for maintenance of Sst+ subtype identity, indicating that subtype maintenance requires active transcriptional programs.

Systems genetics approaches for understanding complex traits with relevance for human disease

Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.

Smurfness-based two-phase model of ageing helps deconvolve the ageing transcriptional signature

Ageing is characterised at the molecular level by six transcriptional 'hallmarks of ageing', that are commonly described as progressively affected as time passes. By contrast, the 'Smurf' assay separates high-and-constant-mortality risk individuals from healthy, zero-mortality risk individuals, based on increased intestinal permeability. Performing whole body total RNA sequencing, we found that Smurfness distinguishes transcriptional changes associated with chronological age from those associated with biological age. We show that transcriptional heterogeneity increases with chronological age in non-Smurf individuals preceding the other five hallmarks of ageing that are specifically associated with the Smurf state. Using this approach, we also devise targeted pro-longevity genetic interventions delaying entry in the Smurf state. We anticipate that increased attention to the evolutionary conserved Smurf phenotype will bring about significant advances in our understanding of the mechanisms of ageing.

Individual cell types in C. elegans age differently and activate distinct cell-protective responses

Aging is characterized by a global decline in physiological function. However, by constructing a complete single-cell gene expression atlas, we find that Caenorhabditis elegans aging is not random in nature but instead is characterized by coordinated changes in functionally related metabolic, proteostasis, and stress-response genes in a cell-type-specific fashion, with downregulation of energy metabolism being the only nearly universal change. Similarly, the rates at which cells age differ significantly between cell types. In some cell types, aging is characterized by an increase in cell-to-cell variance, whereas in others, variance actually decreases. Remarkably, multiple resilience-enhancing transcription factors known to extend lifespan are activated across many cell types with age; we discovered new longevity candidates, such as GEI-3, among these. Together, our findings suggest that cells do not age passively but instead react strongly, and individualistically, to events that occur during aging. This atlas can be queried through a public interface.

Considerations for reproducible omics in aging research

Technical advancements over the past two decades have enabled the measurement of the panoply of molecules of cells and tissues including transcriptomes, epigenomes, metabolomes and proteomes at unprecedented resolution. Unbiased profiling of these molecular landscapes in the context of aging can reveal important details about mechanisms underlying age-related functional decline and age-related diseases. However, the high-throughput nature of these experiments creates unique analytical and design demands for robustness and reproducibility. In addition, 'omic' experiments are generally onerous, making it crucial to effectively design them to eliminate as many spurious sources of variation as possible as well as account for any biological or technical parameter that may influence such measures. In this Perspective, we provide general guidelines on best practices in the design and analysis of omic experiments in aging research from experimental design to data analysis and considerations for long-term reproducibility and validation of such studies.

Strategies of neutrophil diversification

Neutrophils are formidable defenders. Their vast numbers, constant production, high cytotoxicity and capacity to produce extracellular traps, underlie their ability to efficiently protect in a microorganism-rich world. However, neutrophils are much more than immune sentinels, as evidenced by the expanding repertoire of functions discovered in the context of tissue homeostasis, regeneration or chronic pathologies. In this Perspective, we discuss general functional features of the neutrophil compartment that may be relevant in most, if not all, physiological scenarios in which they participate, including specialization in naïve tissues, transcriptional noise in the bloodstream as a potential strategy for diversification and functional bias in inflammatory sites. We intentionally present the reader with more questions than answers and propose models and approaches that we hope will shed new light onto the biology of these fascinating cells and spark new directions of research.

Progress in Discovering Transcriptional Noise in Aging

Increasing stochasticity is a key feature in the aging process. At the molecular level, in addition to genome instability, a well-recognized hallmark of aging, cell-to-cell variation in gene expression was first identified in mouse hearts. With the technological breakthrough in single-cell RNA sequencing, most studies performed in recent years have demonstrated a positive correlation between cell-to-cell variation and age in human pancreatic cells, as well as mouse lymphocytes, lung cells, and muscle stem cells during senescence in vitro. This phenomenon is known as the "transcriptional noise" of aging. In addition to the increasing evidence in experimental observations, progress also has been made to better define transcriptional noise. Traditionally, transcriptional noise is measured using simple statistical measurements, such as the coefficient of variation, Fano factor, and correlation coefficient. Recently, multiple novel methods have been proposed, e.g., global coordination level analysis, to define transcriptional noise based on network analysis of gene-to-gene coordination. However, remaining challenges include a limited number of wet-lab observations, technical noise in single-cell RNA sequencing, and the lack of a standard and/or optimal data analytical measurement of transcriptional noise. Here, we review the recent technological progress, current knowledge, and challenges to better understand transcriptional noise in aging.