Multipotent mesenchymal stromal cells (MSCs) integrate hormone and neuromediator signaling to coordinate tissue homeostasis, tissue renewal and regeneration. To facilitate the investigation of MSC biology, stable immortalized cell lines are created (e.g., commercially available ASC52telo). However, the ASC52telo cell line has an impaired adipogenic ability and a depressed response to hormones, including 5-HT, GABA, glutamate, noradrenaline, PTH and insulin compared to primary cells. This markedly reduces the potential of the ASC52telo cell line in studying the mechanisms of hormonal control of MSC’s physiology. Here, we have established a novel immortalized culture of adipose tissue-derived MSCs via forced telomerase expression after lentiviral transduction. These immortalized cell cultures demonstrate high proliferative potential (up to 40 passages), delayed senescence, as well as preserved primary culture-like functional activity (sensitivity to hormones, ability to hormonal sensitization and differentiation) and immunophenotype up to 17–26 passages. Meanwhile, primary adipose tissue-derived MSCs usually irreversibly lose their properties by 8–10 passages. Observed characteristics of reported immortalized human MSC cultures make them a feasible model for studying molecular mechanisms, which regulate the functional activities of these cells, especially when primary cultures or commercially available cell lines are not appropriate.

Multipotent mesenchymal stromal cells (MSCs) maintain cellular homeostasis and regulate tissue renewal and repair both by differentiating into mesodermal lineage, e.g., adipocytes, or managing the functions of differentiated cells. Insulin is a key physiological inducer of MSC differentiation into adipocytes, and disturbances in MSC insulin sensitivity could negatively affect adipose tissue renewal. During aging, regulation and renewal of adipose tissue cells may be disrupted due to the altered insulin signaling and differentiation potential of senescent MSCs, promoting the development of serious metabolic diseases, including metabolic syndrome and obesity. However, the potential mechanisms mediating the dysfunction of adipose-derived senescent MSC remains unclear. We explored whether aging could affect the adipogenic potential of human adipose tissue-derived MSCs regulated by insulin. Age-associated senescent MSCs (isolated from donors older than 65 years) and MSCs in replicative senescence (long-term culture) were treated by insulin to induce adipogenic differentiation, and the efficiency of the process was compared to MSCs from young donors. Insulin-dependent signaling pathways were explored in these cells. We also analyzed the involvement of extracellular vesicles secreted by MSCs (MSC-EVs) into the regulation of adipogenic differentiation and insulin signaling of control and senescent cells. Also the microRNA profiles of MSC-EVs from aged and young donors were compared using targeted PCR arrays. Both replicatively and chronologically senescent MSCs showed a noticeably decreased adipogenic potential. This was associated with insulin resistance of MSCs from aged donors caused by the increase in the basal level of activation of crucial insulin-dependent intracellular effectors ERK1/2 and Akt. To assess the impact of the paracrine cross-talk of MSCs, we analyzed microRNAs profile differences in MSC-EVs and revealed that senescent MSCs produced EVs with increased content of miRNAs targeting components of insulin-dependent signaling cascade PTEN, MAPK1, GAREM1 and some other targets. We also confirmed these data by differentiation of control MSCs in the presence of EVs from senescent cells and vice versa. Thus, aging attenuated the adipogenic potential of MSCs due to autocrine or paracrine-dependent induction of insulin resistance associated with the specific changes in MSC-EV cargo.

The aim of this study is to conduct a comparative assessment of the effectiveness of neural network models—U-Net, DeepLabV3+, SegNet and Mask R-CNN—for the semantic segmentation of micrographs of human mesenchymal stem cells (MSCs). A dataset of 320 cell micrographs annotated by cell biology experts was created. The models were trained using a transfer learning method based on ImageNet pre-trained weights. As a result, the U-Net model demonstrated the best segmentation accuracy according to the metrics of the Dice coefficient (0.876) and the Jaccard index (0.781). The DeepLabV3+ and Mask R-CNN models also showed high performance, although slightly lower than U-Net, while SegNet exhibited the least accurate results. The obtained data indicate that the U-Net model is the most suitable for automating the segmentation of MSC micrographs and can be recommended for use in biomedical laboratories to streamline the routine analysis of cell cultures.

Non-coding RNA (ncRNAs) genes have attracted increasing attention in recent years due to their widespread involvement in physiological and pathological processes and regulatory networks. The study of the function and molecular partners of ncRNAs opens up opportunities for the early diagnosis and treatment of previously incurable diseases. However, the classical “loss-of-function” approach in ncRNA function analysis is challenged due to some specific issues. Here, we have studied the potency of two CRISPR/Cas9 variants, wild-type (SpCas9wt) and nickase (SpCas9D10A) programmable nucleases, for the editing of extended DNA sequences in human mesenchymal stromal cells (MSCs). Editing the genes of fibrosis-related hsa-miR-21-5p and hsa-miR-29c-3p, we have shown that a pair of SpCas9D10A molecules can effectively disrupt miRNA genes within the genomes of MSCs. This leads not only to a decrease in the level of knockout miRNA in MSCs and MSC-produced extracellular vesicles, but also to a change in cell physiology and the antifibrotic properties of the cell secretome. These changes correlate well with previously published data for the knockdown of certain miRNAs. The proposed approach can be used to knock out ncRNA genes within the genomes of MSCs or similar cell types in order to study their function in biological processes.

In recent years, there has been a significant expansion in the realm of processing microscopy images, thanks to the advent of machine learning techniques. These techniques offer diverse applications for image processing. Currently, numerous methods are employed for processing microscopy images in the field of biology, ranging from conventional machine learning algorithms to sophisticated deep learning artificial neural networks with millions of parameters. However, a comprehensive grasp of the intricacies of these methods usually necessitates proficiency in programming and advanced mathematics. In our comprehensive review, we explore various widely used deep learning approaches tailored for the processing of microscopy images. Our emphasis is on algorithms that have gained popularity in the field of biology and have been adapted to cater to users lacking programming expertise. In essence, our target audience comprises biologists interested in exploring the potential of deep learning algorithms, even without programming skills. Throughout the review, we elucidate each algorithm's fundamental concepts and capabilities without delving into mathematical and programming complexities. Crucially, all the highlighted algorithms are accessible on open platforms without requiring code, and we provide detailed descriptions and links within our review. It's essential to recognize that addressing each specific problem demands an individualized approach. Consequently, our focus is not on comparing algorithms but on delineating the problems they are adept at solving. In practical scenarios, researchers typically select multiple algorithms suited to their tasks and experimentally determine the most effective one. It is worth noting that microscopy extends beyond the realm of biology; its applications span diverse fields such as geology and material science. While our review predominantly centers on biomedical applications, the algorithms and principles outlined here are equally applicable to other scientific domains. Furthermore, a number of the proposed solutions can be modified for use in entirely distinct computer vision cases.

Replicative or stress-induced senescence disrupts the functioning of multipotent mesenchymal stromal cells (MSCs) required for tissue renewal and regeneration. Aged MSCs demonstrate reduced proliferation, impaired differentiation, and aberrant secretory activity, defined as “senescence-associated secretory phenotype” (SASP). SASP is characterized by elevated secretion of proinflammatory cytokines and specific extracellular vesicles (SASP-EVs), which affect the cellular microenvironment and promote tissue dysfunction. However, molecular mechanisms responsible for senescent phenotype propagation remain largely obscure. Earlier, we demonstrated suppression of adipogenic differentiation and insulin sensitivity of young MSCs by SASP-EVs. In this study, we elucidated potential mechanisms underlying SASP-EVs’ effects on MSCs. Bioinformatic analysis revealed that insulin signaling components are the most probable targets of SASP-EVs microRNA cargo. We demonstrated that SASP-EVs downregulated intracellular AGO1 levels, but surprisingly, PTEN levels were upregulated. Specifically, the increase in PTEN content was provided by its nuclear fraction. We have found that the intracellular PTEN distribution in young MSCs treated by SASP-EVs was similar to senescent MSCs. Furthermore, PTEN upregulation was accompanied by increased PTENP1 expression—a molecular sponge for PTEN-targeting microRNAs. Our findings indicate that nuclear PTEN could be a hallmark of senescent MSCs, and SASP-EVs propagate the senescent phenotype in young MSCs by promoting PTEN nuclear localization.