Label-free assessment of replicative senescence in mesenchymal stem cells by Raman microspectroscopy

Use of in vivo Raman spectroscopy and cryoablation for diagnosis and treatment of bladder cancer

Transurethral resection of bladder tumor (TURBT) is the first-line treatment option for non-muscle invasive bladder cancer (NMIBC), but residual tumor often remains after TURBT, thereby leading to cancer recurrence. Here, we introduce combined use of in vivo Raman spectroscopy and in vivo cryoablation as a new approach to detect and remove residual bladder tumor during TURBT. Bladder cancer (BCa) patients treated with TURBT at our urological department between Dec 2019 and Jan 2021 were collected. First, Raman signals were collected from 74 BCa patients to build reference spectra of normal bladder tissue and of bladder cancers of different pathological types. Then, another 53 BCa patients were randomly categorized into two groups, 26 patients accepted traditional TURBT, 27 patients accepted TURBT followed by Raman scanning and cryoablation if Raman detected existence of residual tumor. The recurrence rates of the two groups until Oct 2022 were compared. Raman was capable of discriminating normal bladder tissue and BCa with a sensitivity and specificity of 90.5% and 80.8 %; and discriminating invasive (T1, T2) and noninvasive (Ta) BCa with a sensitivity and specificity of 83.3 % and 87.3 %. During follow-up, 2 in 27 patients had cancer recurrence in Raman-Cryoablation group, while 8 in 26 patients had cancer recurrence in traditional TURBT group. Combined use of Raman and cryoablation significantly reduced cancer recurrence (p = 0.0394). Raman and cryoablation can serve as an adjuvant therapy to TURBT to improve therapeutic effects and reduce recurrence rate.

Morphology-based deep learning enables accurate detection of senescence in mesenchymal stem cell cultures

BACKGROUND

Cell senescence is a sign of aging and plays a significant role in the pathogenesis of age-related disorders. For cell therapy, senescence may compromise the quality and efficacy of cells, posing potential safety risks. Mesenchymal stem cells (MSCs) are currently undergoing extensive research for cell therapy, thus necessitating the development of effective methods to evaluate senescence. Senescent MSCs exhibit distinctive morphology that can be used for detection. However, morphological assessment during MSC production is often subjective and uncertain. New tools are required for the reliable evaluation of senescent single cells on a large scale in live imaging of MSCs.

RESULTS

We have developed a successful morphology-based Cascade region-based convolution neural network (Cascade R-CNN) system for detecting senescent MSCs, which can automatically locate single cells of different sizes and shapes in multicellular images and assess their senescence state. Additionally, we tested the applicability of the Cascade R-CNN system for MSC senescence and examined the correlation between morphological changes with other senescence indicators.

CONCLUSIONS

This deep learning has been applied for the first time to detect senescent MSCs, showing promising performance in both chronic and acute MSC senescence. The system can be a labor-saving and cost-effective option for screening MSC culture conditions and anti-aging drugs, as well as providing a powerful tool for non-invasive and real-time morphological image analysis integrated into cell production.

Raman spectroscopy to assess the differentiation of bone marrow mesenchymal stem cells into a glial phenotype

Background

Mesenchymal stem cells (MSCs) are multipotent precursor cells with the ability to self-renew and differentiate into multiple cell linage, including the Schwann-like fate that promotes regeneration after lesion. Raman spectroscopy provides a precise characterization of the osteogenic, adipogenic, hepatogenic and myogenic differentiation of MSCs. However, the differentiation of bone marrow mesenchymal stem cells (BMSCs) towards a glial phenotype (Schwann-like cells) has not been characterized before using Raman spectroscopy.

Method

We evaluated three conditions: 1) cell culture from rat bone marrow undifferentiated (uBMSCs), and two conditions of differentiation; 2) cells exposed to olfactory ensheathing cells-conditioned medium (dBMSCs) and 3) cells obtained from olfactory bulb (OECs). uBMSCs phenotyping was confirmed by morphology, immunocytochemistry and flow cytometry using antibodies of cell surface: CD90 and CD73. Glial phenotype of dBMSCs and OECs were verified by morphology and immunocytochemistry using markers of Schwann-like cells and OECs such as GFAP, p75 NTR and O4. Then, the Principal Component Analysis (PCA) of Raman spectroscopy was performed to discriminate components from the high wavenumber region between undifferentiated and glial-differentiated cells. Raman bands at the fingerprint region also were used to analyze the differentiation between conditions.

Results

Differences between Raman spectra from uBMSC and glial phenotype groups were noted at multiple Raman shift values. A significant decrease in the concentration of all major cellular components, including nucleic acids, proteins, and lipids were found in the glial phenotype groups. PCA analysis confirmed that the highest spectral variations between groups came from the high wavenumber region observed in undifferentiated cells and contributed with the discrimination between glial phenotype groups.

Conclusion

These findings support the use of Raman spectroscopy for the characterization of uBMSCs and its differentiation in the glial phenotype.

New Possibilities for Evaluating the Development of Age-Related Pathologies Using the Dynamical Network Biomarkers Theory

Aging is the slowest process in a living organism. During this process, mortality rate increases exponentially due to the accumulation of damage at the cellular level. Cellular senescence is a well-established hallmark of aging, as well as a promising target for preventing aging and age-related diseases. However, mapping the senescent cells in tissues is extremely challenging, as their low abundance, lack of specific markers, and variability arise from heterogeneity. Hence, methodologies for identifying or predicting the development of senescent cells are necessary for achieving healthy aging. A new wave of bioinformatic methodologies based on mathematics/physics theories have been proposed to be applied to aging biology, which is altering the way we approach our understand of aging. Here, we discuss the dynamical network biomarkers (DNB) theory, which allows for the prediction of state transition in complex systems such as living organisms, as well as usage of Raman spectroscopy that offers a non-invasive and label-free imaging, and provide a perspective on potential applications for the study of aging.

Noninvasive morpho-molecular imaging reveals early therapy-induced senescence in human cancer cells

Anticancer therapy screening in vitro identifies additional treatments and improves clinical outcomes. Systematically, although most tested cells respond to cues with apoptosis, an appreciable portion enters a senescent state, a critical condition potentially driving tumor resistance and relapse. Conventional screening protocols would strongly benefit from prompt identification and monitoring of therapy-induced senescent (TIS) cells in their native form. We combined complementary all-optical, label-free, and quantitative microscopy techniques, based on coherent Raman scattering, multiphoton absorption, and interferometry, to explore the early onset and progression of this phenotype, which has been understudied in unperturbed conditions. We identified TIS manifestations as early as 24 hours following treatment, consisting of substantial mitochondrial rearrangement and increase of volume and dry mass, followed by accumulation of lipid vesicles starting at 72 hours. This work holds the potential to affect anticancer treatment research, by offering a label-free, rapid, and accurate method to identify initial TIS in tumor cells.

Deep ensemble learning and transfer learning methods for classification of senescent cells from nonlinear optical microscopy images

The success of chemotherapy and radiotherapy anti-cancer treatments can result in tumor suppression or senescence induction. Senescence was previously considered a favorable therapeutic outcome, until recent advancements in oncology research evidenced senescence as one of the culprits of cancer recurrence. Its detection requires multiple assays, and nonlinear optical (NLO) microscopy provides a solution for fast, non-invasive, and label-free detection of therapy-induced senescent cells. Here, we develop several deep learning architectures to perform binary classification between senescent and proliferating human cancer cells using NLO microscopy images and we compare their performances. As a result of our work, we demonstrate that the most performing approach is the one based on an ensemble classifier, that uses seven different pre-trained classification networks, taken from literature, with the addition of fully connected layers on top of their architectures. This approach achieves a classification accuracy of over 90%, showing the possibility of building an automatic, unbiased senescent cells image classifier starting from multimodal NLO microscopy data. Our results open the way to a deeper investigation of senescence classification via deep learning techniques with a potential application in clinical diagnosis.

Raman spectroscopy differ leukemic cells from their healthy counterparts and screen biomarkers in acute leukemia

Precision medicine is important in the treatment of acute leukemia (AL). The target therapies of AL provide an opportunity to reduce the mortality of AL. How AL cells differ from their healthy counterparts is the basis for the development of therapies and the outcome of AL patients. Therefore, a label-free and noninvasive single-cell Raman platform was used to characterize cell molecular profiles and found potential biomarkers from three healthy people and twelve AL patients with more than 90% accuracy. We analyzed myeloblasts, abnormal promyelocytes, monoblasts and B-ALL cells respectively, compared with their healthy counterparts, which could be distinguished by their intrinsic phenotypic Raman spectra using orthogonal partial least squares discriminate analysis (OPLS-DA). Most importantly, we selected statistically significant markers of the four leukemia models. Further analysis of leukemic granulocytes, we found that a combination of the 1003, 1341 and 1579 cm^-1 Raman peaks could discriminate myeloblasts and abnormal promyelocytes from normal granulocytes. The assignments of 1579 cm^-1 gave us a clue to find potential important variables myeloperoxidase related with AL diagnosis. Our study demonstrates the capability of the Raman platform to characterize leukemia cells with non-invasively probing metabolites. The biomarker we identified could be extensible to other blood cells and potentially have a high impact on leukemia therapy.

Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals

In this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model's fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.

Raman spectroscopy assisted biochemical evaluation of L929 fibroblast cells on differentially crosslinked gelatin hydrogels

Biochemical evaluation of cell-matrix interaction using conventional labelling techniques often possesses limitations due to dye entrapment. In contrast, Raman spectroscopy guided approach offers label-free determination of cell-matrix biochemistry. Herein, gelatin (Gel) matrices modified with 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide/ N-Hydroxysuccinimide (EDC/NHS) and glutaraldehyde (GTA) was used as standards for comparative evaluation. Raman spectroscopy was deployed as a label-free approach to investigate interaction of cells with Gel hydrogels. Raman-based approach assisted in evaluation of cell-matrix interactions by identifying key biomolecular signatures retrospecting the fact that L929 fibroblast cells portrayed excellent growth and proliferation kinetics in crosslinked Gel as compared to its bare counterpart. EDC crosslinked hydrogels exhibited superior cell proliferation than its GTA counterparts. Cell proliferation on differentially crosslinked gel was also confirmed using standard MTT Assay and Rhodamine-DAPI staining thus corroborating the fact that Raman spectroscopy can be deployed as a superior label-free alternative towards real-time determination of cell proliferation and growth.

Single-Cell Multimodal Analytical Approach by Integrating Raman Optical Tweezers and RNA Sequencing

Single-cell analysis has become a state-of-art approach to heterogeneity profiling in tumor cells. Herein, we realize a kind of single-cell multimodal analytical approach by combining single-cell RNA sequencing (scRNA-seq) with Raman optical tweezers (ROT), a label-free single-cell identification and isolation technique, and apply it to investigate drug sensitivity. The drug sensitivity of human BGC823 gastric cancer cells toward different drugs, paclitaxel and sodium dichloroacetate, was distinguished in the conjoint analytical way including morphology monitoring, Raman identification, and transcriptomic profiling. Each individual BGC823 cancer cell was measured by Raman spectroscopy, then nondestructively isolated out by ROT, and finally RNA-sequenced. Our results demonstrate each analytical mode can reflect cell response to the drugs from different perspectives and is consistent and complementary with each other. Therefore, we believe the multimodal analytical approach offers an access to comprehensive characterizations of the unicellular complexity, which especially makes sense for studying tumor heterogeneity or a desired special cell from a mixture cell sample such as whole blood.

Discrimination of Dental Pulp Stem Cell Regenerative Heterogeneity by Single-Cell Raman Spectroscopy

IMPACT STATEMENT

This study is the first to investigate and confirm the effectiveness of single-cell Raman spectroscopy (SCRM), in its ability to discriminate between dental pulp stem cells (DPSCs) with contrasting proliferative and differentiation capabilities. The findings show that SCRM can rapidly and noninvasively distinguish and identify DPSC subpopulations in vitro with superior proliferative and multipotency properties, versus lesser quality DPSCs, thereby overcoming the significant heterogeneity issues surrounding DPSC ex vivo expansion and differentiation capabilities. Such findings support further SCRM assessment for the selective screening/isolation of superior quality DPSCs from whole dental pulp tissues, for more effective in vitro evaluation and therapy development.

Raman fingerprints as promising markers of cellular senescence and aging

Due to our aging population, understanding of the underlying molecular mechanisms constantly gains more and more importance. Senescent cells, defined by being irreversibly growth arrested and associated with a specific gene expression and secretory pattern, accumulate with age and thus contribute to several age-related diseases. However, their specific detection, especially in vivo, is still a major challenge. Raman microspectroscopy is able to record biochemical fingerprints of cells and tissues, allowing a distinction between different cellular states, or between healthy and cancer tissue. Similarly, Raman microspectroscopy was already successfully used to distinguish senescent from non-senescent cells, as well as to investigate other molecular changes that occur at cell and tissue level during aging. This review is intended to give an overview about various applications of Raman microspectroscopy to study aging, especially in the context of detecting senescent cells.

Applications of Raman spectroscopy in the development of cell therapies: state of the art and future perspectives

This comprehensive review article discusses current and future perspectives of Raman spectroscopy-based analyses of cell therapy processes and products.

Identification of senescent cells in multipotent mesenchymal stromal cell cultures: Current methods and future directions

Regardless of their tissue of origin, multipotent mesenchymal stromal cells (MSCs) are commonly expanded in vitro for several population doublings to achieve a sufficient number of cells for therapy. Prolonged MSC expansion has been shown to result in phenotypical, morphological and gene expression changes in MSCs, which ultimately lead to the state of senescence. The presence of senescent cells in therapeutic MSC batches is undesirable because it reduces their viability, differentiation potential and trophic capabilities. Additionally, senescent cells acquire senescence-activated secretory phenotype, which may not only induce apoptosis in the neighboring host cells following MSC transplantation, but also trigger local inflammatory reactions. This review outlines the current and promising new methodologies for the identification of senescent cells in MSC cultures, with a particular emphasis on non-destructive and label-free methodologies. Technologies allowing identification of individual senescent cells, based on new surface markers, offer potential advantage for targeted senescent cell removal using new-generation senolytic agents, and subsequent production of therapeutic MSC batches fully devoid of senescent cells. Methods or a combination of methods that are non-destructive and label-free, for example, involving cell size and spectroscopic measurements, could be the best way forward because they do not modify the cells of interest, thus maximizing the final output of therapeutic-grade MSC cultures. The further incorporation of machine learning methods has also recently shown promise in facilitating, automating and enhancing the analysis of these measured data.

Anti-senescence effect and molecular mechanism of the major royal jelly proteins on human embryonic lung fibroblast (HFL-I) cell line

Royal jelly (RJ) from honeybee has been widely used as a health promotion supplement. The major royal jelly proteins (MRJPs) have been identified as the functional component of RJ. However, the question of whether MRJPs have anti-senescence activity for human cells remains. Human embryonic lung fibroblast (HFL-I) cells were cultured in media containing no MRJPs (A), MRJPs at 0.1 mg/ml (B), 0.2 mg/ml (C), or 0.3 mg/ml (D), or bovine serum albumin (BSA) at 0.2 mg/ml (E). The mean population doubling levels of cells in media B, C, D, and E were increased by 12.4%, 31.2%, 24.0%, and 10.4%, respectively, compared with that in medium A. The cells in medium C also exhibited the highest relative proliferation activity, the lowest senescence, and the longest telomeres. Moreover, MRJPs up-regulated the expression of superoxide dismutase-1 (SOD1) and down-regulated the expression of mammalian target of rapamycin (MTOR), catenin beta like-1 (CTNNB1), and tumor protein p53 (TP53). Raman spectra analysis showed that there were two unique bands related to DNA synthesis materials, amide carbonyl group vibrations and aromatic hydrogens. These results suggest that MRJPs possess anti-senescence activity for the HFL-I cell line, and provide new knowledge illustrating the molecular mechanism of MRJPs as anti-senescence factors.