Table 1.

List of studies aimed at bone senescence.

Study Summary	NGS Methods	Key Findings	Key Genes with Elevated Expression	Reference
RNA profile expression in eight experimental models of cellular senescence commonly studied. WI-38 and IMR-90 fibroblasts, HUVECs and HAECs were cultured and utilized for downstream sequencing.	RNA seq, Illumina Hiseq 2500 with 2 × 150-bp strategy	50 elevated and 18 reduced transcripts and the identification of subsets of transcripts (both coding and non-coding) display shared expression patterns across a range of senescent cell models.	SRPX, PURPL (p53 regulator)	[111]
RNA expression profiling of fibroblasts and their senescence induced by 5-aza identified 3 epigenetically silenced pathways.	Illumina HiSeq 2000; strategy not specified	5-aza-induced senescence has been closely linked to alterations in the interferon/innate immunity pathway’s gene expression, and during immortalization, important regulators of this pathway are muted.	IL-1α and IL-1β	[112]
Use of multiple whole-transcriptome datasets, created by the authors or made publicly available, to characterise the heterogeneity of the programmed senescence.	Illumina HiSeq 2000 with 2× 150 bp	Demonstrates that ollowing senescence induction, the senescent phenotype identified for 55 genes at the core of the senescence-associated transcriptome is dynamic, changing at different intervals.	Upregulation of genes associated with G1 DNA damage checkpoint (PLK3 and CCND1) and upregulation of BCL2L2 (negative regulator of apoptosis)	[113]
Comprehensive analysis of the transcriptome and senolytic responses in a panel of 13 cancer cell lines rendered senescent by two distinct compounds.	HiSeq 2500; single-end 65 bp	Cell lines that were made senescent by two different substances showed that the senescence trigger has less of an impact on the composition of the SASP. The SENCAN gene expression classifier to detect senescence using machine learning was developed.	IL6 and CXCL8	[114]
Gene set generation (SenMayo) consisting of 125 previously identified senescence/SASP-associated factors.	HiSeq 2000; strategy not specified	Provided a unique gene set (SenMayo) that can be utilized in bulk and scRNA-seq investigations to detect cells expressing high amounts of senescence/SASP genes. SenMayo rises with aging across tissues and species and is responsive to senescent cell clearance.	CDKN1A/P21Cip1 and several SASP markers such as CCL2 and IL6 showed consistent upregulation with aging	[102]