Background: Regenerative medicine is evolving with discoveries like the stromal vascular fraction (SVF), a diverse cell group from adipose tissue with therapeutic promise. Originating from fat cell metabolism studies in the 1960s, SVF\'s versatility was recognized after demonstrating multipotency. Comprising of cells like pericytes, smooth muscle cells, and, notably, adipose-derived stem cells (ADSCs), SVF offers tissue regeneration and repair through the differentiation and secretion of growth factors. Its therapeutic efficacy is due to these cells\' synergistic action, prompting extensive research. Methods: This review analyzed the relevant literature on SVF, covering its composition, action mechanisms, clinical applications, and future directions. An extensive literature search from January 2018 to June 2023 was conducted across databases like PubMed, Embase, etc., using specific keywords. Results: The systematic literature search yielded a total of 473 articles. Sixteen articles met the inclusion criteria and were included in the review. This rigorous methodology provides a framework for a thorough and systematic analysis of the existing literature on SVF, offering robust insights into the potential of this important cell population in regenerative medicine. Conclusions: Our review reveals the potential of SVF, a heterogeneous cell mixture, as a powerful tool in regenerative medicine. SVF has demonstrated therapeutic efficacy and safety across disciplines, improving pain, tissue regeneration, graft survival, and wound healing while exhibiting immunomodulatory and anti-inflammatory properties.

In the last two decades, many detailed full transcriptomic studies on complex biological samples have been published and included in large gene expression repositories. These studies primarily provide a bulk expression signal for each sample, including multiple cell-types mixed within the global signal. The cellular heterogeneity in these mixtures does not allow the activity of specific genes in specific cell types to be identified. Therefore, inferring relative cellular composition is a very powerful tool to achieve a more accurate molecular profiling of complex biological samples. In recent decades, computational techniques have been developed to solve this problem by applying deconvolution methods, designed to decompose cell mixtures into their cellular components and calculate the relative proportions of these elements. Some of them only calculate the cell proportions (supervised methods), while other deconvolution algorithms can also identify the gene signatures specific for each cell type (unsupervised methods). In these work, five deconvolution methods (CIBERSORT, FARDEEP, DECONICA, LINSEED and ABIS) were implemented and used to analyze blood and immune cells, and also cancer cells, in complex mixture samples (using three bulk expression datasets). Our study provides three analytical tools (corrplots, cell-signature plots and bar-mixture plots) that allow a thorough comparative analysis of the cell mixture data. The work indicates that CIBERSORT is a robust method optimized for the identification of immune cell-types, but not as efficient in the identification of cancer cells. We also found that LINSEED is a very powerful unsupervised method that provides precise and specific gene signatures for each of the main immune cell types tested: neutrophils and monocytes (of the myeloid lineage), B-cells, NK cells and T-cells (of the lymphoid lineage), and also for cancer cells.

Modern epigenetics emerged about 40 years ago. Since then, the field has rapidly grown. Unfortunately, this development has been accompanied by certain misconceptions and methodological shortcomings. A profound misconception is that chromatin modifications are a distinct layer of gene regulation that is directly responsive to the environment and potentially heritable between generations. This view ignores the fact that environmental factors affect gene expression mainly through signaling cascades and the activation or repression of transcription factors, which recruit chromatin regulators. The epigenome is mainly shaped by the DNA sequence and by transcription. Methodological shortcomings include the insufficient consideration of genetic variation and cell mixture distribution. Mis- and overinterpretation of epigenetic data foster genetic denialism (\"We can control our genes\") and epigenetic determinism (\"You are what your parents ate\"). These erroneous beliefs can be overcome by using precise definitions, by raising the awareness about methodological pitfalls and by returning to the basic facts in molecular and cellular biology.

One problem that plagues epigenome-wide association studies is the potential confounding due to cell mixtures when purified target cells are not available. Reference-free adjustment of cell mixtures has become increasingly popular due to its flexibility and simplicity. However, existing methods are still not optimal: increased false positive rates and reduced statistical power have been observed in many scenarios.
We develop SmartSVA, an optimized surrogate variable analysis (SVA) method, for fast and robust reference-free adjustment of cell mixtures. SmartSVA corrects the limitation of traditional SVA under highly confounded scenarios by imposing an explicit convergence criterion and improves the computational efficiency for large datasets.
Compared to traditional SVA, SmartSVA achieves an order-of-magnitude speedup and better false positive control. It protects the signals when capturing the cell mixtures, resulting in significant power increase while controlling for false positives. Through extensive simulations and real data applications, we demonstrate a better performance of SmartSVA than the existing methods.
SmartSVA is a fast and robust method for reference-free adjustment of cell mixtures for epigenome-wide association studies. As a general method, SmartSVA can be applied to other genomic studies to capture unknown sources of variability.