FUCCI Real-Time Cell-Cycle Imaging as a Guide for Designing Improved Cancer Therapy: A Review of Innovative Strategies to Target Quiescent Chemo-Resistant Cancer Cells

Identification and Verification of a Glycolysis-Related lncRNA Prognostic Signature for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a primary liver cancer with a high mortality rate. The search for a new biomarker could help the prognosis of HCC patients. We identified the glycolytic gene set associated with HCC and the glycolytic lncRNA based on TCGA and MsigDB databases. According to these lncRNAs, K-means clustering, and regression analysis were performed on the patients. Two groups of HCC patients with different lncRNA expression levels were obtained based on K-means clustering results. The results of difference analysis and enrichment analysis showed that DEmRNA in the two HCC populations with significant survival differences was mainly enriched in transmembrane transporter complex, RNA polymerase II specificity, cAMP signaling pathway, and calcium signaling pathway. In addition, a prognostic model of HCC with 4 DElncRNAs was constructed based on regression analysis. ROC curve analysis showed that the model had good predictive performance. Drug predictionresults showed that the efficacy of JQ1, niraparib, and teniposide was higher in the low-risk group than in the high-risk group. In conclusion, this study preliminarily identified glycolytic-related prognostic features of lncRNAs in HCC and constructed a risk assessment model. The results of this study are expected to guide the prognosis assessment of clinical HCC patients.

An electroporation cytometry system for long-term, live cell cycle analysis

Electric fields are used in biology to address a broad range of questions and through a variety of techniques, including electroporation, gene electrotransfer (GET), electrostimulation (ES), and electrochemotherapy. Each of these modalities requires specific conditions and has drastically different target outcomes on the cell. ES has demonstrated that non-pore forming electric fields alter cell cycle progression. However, pore forming electric fields such as with GET have not been as widely explored despite major clinical advancements. Additionally, the real-time visual analysis of electrical field effects on mammalian cell culture is currently lacking among most commercial systems. To facilitate investigations into these research areas, an electroporation cytometry system was developed including a custom chamber compatible with live cell imaging and exponential decay pulse generator for live cell analysis. The functionality of the system was demonstrated using a recombinant cell line using U-2 OS cells and FUCCI(CA)5 cell cycle reporter. The exposure of the cells to a 180 V pulse in both unsynchronized and synchronized populations revealed an effect on the cell cycle.

Accurate and efficient integrative reference-informed spatial domain detection for spatial transcriptomics

Spatially resolved transcriptomics (SRT) studies are becoming increasingly common and large, offering unprecedented opportunities in mapping complex tissue structures and functions. Here we present integrative and reference-informed tissue segmentation (IRIS), a computational method designed to characterize tissue spatial organization in SRT studies through accurately and efficiently detecting spatial domains. IRIS uniquely leverages single-cell RNA sequencing data for reference-informed detection of biologically interpretable spatial domains, integrating multiple SRT slices while explicitly considering correlations both within and across slices. We demonstrate the advantages of IRIS through in-depth analysis of six SRT datasets encompassing diverse technologies, tissues, species and resolutions. In these applications, IRIS achieves substantial accuracy gains (39-1,083%) and speed improvements (4.6-666.0) in moderate-sized datasets, while representing the only method applicable for large datasets including Stereo-seq and 10x Xenium. As a result, IRIS reveals intricate brain structures, uncovers tumor microenvironment heterogeneity and detects structural changes in diabetes-affected testis, all with exceptional speed and accuracy.

ConfluentFUCCI for fully-automated analysis of cell-cycle progression in a highly dense collective of migrating cells

Understanding mechanisms underlying various physiological and pathological processes often requires accurate and fully automated analysis of dense cell populations that collectively migrate. In such multicellular systems, there is a rising interest in the relations between biophysical and cell cycle progression aspects. A seminal tool that led to a leap in real-time study of cell cycle is the fluorescent ubiquitination-based cell cycle indicator (FUCCI). Here, we introduce ConfluentFUCCI, an open-source graphical user interface-based framework that is designed, unlike previous tools, for fully automated analysis of cell cycle progression, cellular dynamics, and cellular morphology, in highly dense migrating cell collectives. We integrated into ConfluentFUCCI's pipeline state-of-the-art tools such as Cellpose, TrackMate, and Napari, some of which incorporate deep learning, and we wrap the entire tool into an isolated computational environment termed container. This provides an easy installation and workflow that is independent of any specific operation system. ConfluentFUCCI offers accurate nuclear segmentation and tracking using FUCCI tags, enabling comprehensive investigation of cell cycle progression at both the tissue and single-cell levels. We compare ConfluentFUCCI to the most recent relevant tool, showcasing its accuracy and efficiency in handling large datasets. Furthermore, we demonstrate the ability of ConfluentFUCCI to monitor cell cycle transitions, dynamics, and morphology within densely packed epithelial cell populations, enabling insights into mechanotransductive regulation of cell cycle progression. The presented tool provides a robust approach for investigating cell cycle-related phenomena in complex biological systems, offering potential applications in cancer research and other fields.

Identification of prognostic risk model based on plasma cell markers in hepatocellular carcinoma through single-cell sequencing analysis

Hepatocellular carcinoma (HCC) represents a substantial global health burden. Tumorinfiltrating B lymphocytes (TIL-Bs) contribute to tumor progression and significantly impact the efficacy of tumor therapy. However, the characteristics of TIL-Bs in HCC and their effect on HCC therapy remain elusive. Single-cell RNA sequencing (scRNAseq) was applied to investigate the heterogeneity, cellular differentiation and cell-cell communication of TIL-Bs in HCC. Further, the Cancer Genome Atlas-liver hepatocellular carcinoma (TCGA-LIHC) and liver cancer institutes (LCI) cohorts were applied to construct and validate the plasma cell marker-based prognostic risk model. The relationship between the prognostic risk model and the responsiveness of immunotherapy and chemotherapy in patients with HCC were estimated by OncoPredict and tumor immune dysfunction and exclusion (TIDE) algorithm. Finally, we established nomogram and calibration curves to evaluate the precision of the risk score in predicating survival probability. Our data identified five subtypes of TIL-Bs in HCC, each exhibiting varying levels of infiltration in tumor tissues. The interactions between TIL-Bs and other cell types contributed to shaping distinct tumor microenvironments (TME). Moreover, we found that TIL-Bs subtypes had disparate prognostic values in HCC patients. The prognostic risk model demonstrated exceptional predictive accuracy for overall survival and exhibited varying sensitivities to immunotherapy and chemotherapy among patients with HCC. Our data demonstrated that the risk score stood as an independent prognostic predictor and the nomogram results further affirmed its strong prognostic capability. This study reveals the heterogeneity of TIL-Bs and provides a prognostic risk model based on plasma cell markers in HCC, which could prove valuable in predicting prognosis and guiding the choice of suitable therapies for patients with HCC.

Cell cycle dependence of cell survival following exposure to X-rays in synchronous HeLa cells expressing fluorescent ubiquitination-based cell cycle indicators

The cell cycle dependence of radiosensitivity has yet to be fully determined, as it is technically difficult to achieve a high degree of cell cycle synchronization in cultured cell systems and accurately detect the cell cycle phase of individual cells simultaneously. We used human cervical carcinoma HeLa cells expressing fluorescent ubiquitination-based cell cycle indicators (FUCCI), and employed the mitotic harvesting method that is one of the cell cycle synchronization methods. The imaging analysis confirmed that the cell cycle is highly synchronized after mitotic cell harvesting until 18-20 h of the doubling time has elapsed. Also, flow cytometry analysis revealed that the S and G2 phases peak at approximately 12 and 14-16 h, respectively, after mitotic harvesting. In addition, the clonogenic assay showed the changes in surviving fractions following exposure to X-rays according to the progress through the cell cycle. These results indicate that HeLa-FUCCI cells become radioresistant in the G1 phase, become radiosensitive in the early S phase, rapidly become radioresistant in the late S phase, and become radiosensitive again in the G2 phase. Our findings may contribute to the further development of combinations of radiation and cell cycle-specific anticancer agents.

A semi-automated microscopic image analysis method for scoring Ki-67 nuclear immunostaining

Nuclear proliferation marker MIB-1 (Ki-67) immunohistochemistry (IHC) is used to examine tumor cell proliferation. However, the diagnostic or prognostic value of the Ki-67 nuclear staining intensity and location, defined as nuclear gradient (NG), has not been assessed. This study examined the potential association between Ki-67 NG and cell cycle phases and its effect on the prognosis of pulmonary typical carcinoid (PTC) tumors. We propose a method for classifying the NG of Ki-67 during the cell cycle and compare the results between PTC, pulmonary adenocarcinoma (PAD), and breast ductal carcinoma (BDC). A literature review and objective analysis of IHC-stained paraffin sections were used to determine the Ki-67 labeling index and composed a stratification of the NG into NG1, NG2, and NG3/4 categories. A semi-automated image analysis protocol was established to determine the Ki-67 NG in PTC, PAD, and BDC. High intraobserver consistency and moderate interobserver agreement were achieved in the determination of Ki-67 NG in tumor specimens. NG1 and NG2 were lower in PTC than in PAD and BDC. Cox multivariate analysis of PTC after adjusting for age and number of metastatic lymph nodes showed that Ki-67 NG1 and NG2 significantly predicted clinical outcomes. The semi-automated method for quantification of Ki-67 nuclear immunostaining proposed in this study could become a valuable diagnostic and prognostic tool in PTC.

Bioorthogonal surface-enhanced Raman scattering flower-like nanoprobe with embedded standards for accurate cancer cell imaging

Developing precise and effective strategies for cancer identification and imaging is attractive due to their importance for early cancer detection, prognosis, and subsequent treatment. Herein, we reported a novel bioorthogonal surface-enhanced Raman scattering (SERS) nanoprobe for accurate cancer cell imaging. A novel core-molecule-shell nanoflower (Au@4-MBN@Au) with rich electromagnetic hot spots and enhanced Raman scattering was first synthesized by optimizing the embedded concentrations of 4-mercaptobenzonitrile (4-MBN). Then, Au@4-MBN@Au was further modified with FA-PEG-SH molecules to acquire the bioorthogonal SERS nanoprobe Au@4-MBN@Au-PEG-FA. The SERS nanoprobe illustrated a robust and stable nitrile stretching vibration Raman signal (2223 cm^-1) in the cellular silent region, ensuring high sensitivity and ultra-accuracy SERS imaging of cancer cells. Furthermore, cell imaging results demonstrated Au@4-MBN@Au-PEG-FA could recognize FR-positive HeLa cells with high selectivity due to the high affinity between folate receptor and folic acid. More notably, Au@4-MBN@Au-PEG-FA has been applied to identify FR-positive Hela cells from co-cultured cancer cells with similar morphology by SERS imaging for the first time. With improved signal-to-background ratio, high selectivity, and excellent stability, we anticipate the SERS nanoprobe Au@4-MBN@Au-PEG-FA could be applied for FR-related cancer theranostics and clinical detection in the future.

Regulation of dormancy during tumor dissemination: the role of the ECM

The study of the metastatic cascade has revealed the complexity of the process and the multiple cellular states that disseminated cancer cells must go through. The tumor microenvironment and in particular the extracellular matrix (ECM) plays an important role in regulating the transition from invasion, dormancy to ultimately proliferation during the metastatic cascade. The time delay from primary tumor detection to metastatic growth is regulated by a molecular program that maintains disseminated tumor cells in a non-proliferative, quiescence state known as tumor cell dormancy. Identifying dormant cells and their niches in vivo and how they transition to the proliferative state is an active area of investigation, and novel approaches have been developed to track dormant cells during dissemination. In this review, we highlight the latest research on the invasive nature of disseminated tumor cells and their link to dormancy programs. We also discuss the role of the ECM in sustaining dormant niches at distant sites.

Cell Phase Identification in a Three-Dimensional Engineered Tumor Model by Infrared Spectroscopic Imaging

Cell cycle progression plays a vital role in regulating proliferation, metabolism, and apoptosis. Three-dimensional (3D) cell cultures have emerged as an important class of in vitro disease models, and incorporating the variation occurring from cell cycle progression in these systems is critical. Here, we report the use of Fourier transform infrared (FT-IR) spectroscopic imaging to identify subtle biochemical changes within cells, indicative of the G1/S and G2/M phases of the cell cycle. Following previous studies, we first synchronized samples from two-dimensional (2D) cell cultures, confirmed their states by flow cytometry and DNA quantification, and recorded spectra. We determined two critical wavenumbers (1059 and 1219 cm-1) as spectral indicators of the cell cycle for a set of isogenic breast cancer cell lines (MCF10AT series). These two simple spectral markers were then applied to distinguish cell cycle stages in a 3D cell culture model using four cell lines that represent the main stages of cancer progression from normal cells to metastatic disease. Temporal dependence of spectral biomarkers during acini maturation validated the hypothesis that the cells are more proliferative in the early stages of acini development; later stages of the culture showed stability in the overall composition but unique spatial differences in cells in the two phases. Altogether, this study presents a computational and quantitative approach for cell phase analysis in tissue-like 3D structures without any biomarker staining and provides a means to characterize the impact of the cell cycle on 3D biological systems and disease diagnostic studies using IR imaging.

Proposal for a New Diagnostic Histopathological Approach in the Evaluation of Ki-67 in GEP-NETs

Introduction: Studies have shown that the Ki-67 index is a valuable biomarker for the diagnosis, and classification of gastro-entero-pancreatic neuroendocrine tumors (GEP-NETs). We re-evaluated the expression of Ki-67 based on the intensity of the stain, basing our hypothesis on the fact that the Ki-67 protein is continuously degraded. Background: The aim was to evaluate whether a new scoring method would be more effective in classifying NETs by reducing staining heterogeneity. Methods: Patients with GEP-NET (n = 87) were analyzed. The classification difference between the two methods was determined. Results: The classification changed significantly when the Ki-67 semiquantal index was used. The percentage of G1 patients increased from 18.4% to 60.9%, while the G2 patients decreased from 66.7% to 29.9% and the G3 patients also decreased from 14.9% to 9.2%. Moreover, it was found that the traditional Ki-67 was not significantly related to the overall survival (OS), whereas the semiquantal Ki-67 was significantly related to the OS. Conclusions: The new quantification was a better predictor of OS and of tumor classification. Therefore, it could be used both as a marker of proliferation and as a tool to map tumor dynamics that can influence the diagnosis and guide the choice of therapy.

Designing and interpreting 4D tumour spheroid experiments

Tumour spheroid experiments are routinely used to study cancer progression and treatment. Various and inconsistent experimental designs are used, leading to challenges in interpretation and reproducibility. Using multiple experimental designs, live-dead cell staining, and real-time cell cycle imaging, we measure necrotic and proliferation-inhibited regions in over 1000 4D tumour spheroids (3D space plus cell cycle status). By intentionally varying the initial spheroid size and temporal sampling frequencies across multiple cell lines, we collect an abundance of measurements of internal spheroid structure. These data are difficult to compare and interpret. However, using an objective mathematical modelling framework and statistical identifiability analysis we quantitatively compare experimental designs and identify design choices that produce reliable biological insight. Measurements of internal spheroid structure provide the most insight, whereas varying initial spheroid size and temporal measurement frequency is less important. Our general framework applies to spheroids grown in different conditions and with different cell types.

The cancer-inhibitory effects of proliferating tumor-residing fibroblasts

Initiation, local progression, and metastasis of cancer are associated with specific morphological, molecular, and functional changes in the extracellular matrix and the fibroblasts within the tumor microenvironment (TME). In the early stages of tumor development, fibroblasts are an obstacle that cancer cells must surpass or nullify to progress. Thus, in early tumor progression, specific signaling from cancer cells activates bio-pathways, which abolish the innate anticancer properties of fibroblasts and convert a high proportion of them to tumor-promoting cancer-associated fibroblasts (CAFs). Following this initial event, a wide spectrum of gene expression changes gradually leads to the development of a stromal fibroblast population with complex heterogeneity, creating fibroblast subtypes with characteristic profiles, which may alternate between being tumor-promotive and tumor-suppressive, topologically and chronologically in the TME. These fibroblast subtypes form the tumor's histological landscape including areas of cancer growth, inflammation, angiogenesis, invasion fronts, proliferating and non-proliferating fibroblasts, cancer-cell apoptosis, fibroblast apoptosis, and necrosis. These features reflect general deregulation of tissue homeostasis within the TME. This review discusses fundamental and current knowledge that has established the existence of anticancer fibroblasts within the various interacting elements of the TME. It is proposed that the maintenance of fibroblast proliferation is an essential parameter for the activation of their anticancer capacity, similar to that by which normal fibroblasts would be activated in wound repair, thus maintaining tissue homeostasis. Encouragement of research in this direction may render new means of cancer therapy and a greater understanding of tumor progression.

Bioinformatics prediction of differential miRNAs in non-small cell lung cancer

Background

Non-small cell lung cancer (NSCLC) accounts for 85% of all lung cancers. The drug resistance of NSCLC has clinically increased. This study aimed to screen miRNAs associated with NSCLC using bioinformatics analysis. We hope that the screened miRNA can provide a research direction for the subsequent treatment of NSCLC.

Methods

We screened out the common miRNAs after compared the NSCLC-related genes in the TCGA database and GEO database. Selected miRNA was performed ROC analysis, survival analysis, and enrichment analysis (GO term and KEGG pathway).

Results

A total of 21 miRNAs were screened in the two databases. And they were all highly expressed in normal and low in cancerous tissues. Hsa-mir-30a was selected by ROC analysis and survival analysis. Enrichment analysis showed that the function of hsa-mir-30a is mainly related to cell cycle regulation and drug metabolism.

Conclusion

Our study found that hsa-mir-30a was differentially expressed in NSCLC, and it mainly affected NSCLC by regulating the cell cycle and drug metabolism.

Osmotic pressure modulates single cell cycle dynamics inducing reversible growth arrest and reactivation of human metastatic cells

Biophysical cues such as osmotic pressure modulate proliferation and growth arrest of bacteria, yeast cells and seeds. In tissues, osmotic regulation takes place through blood and lymphatic capillaries and, at a single cell level, water and osmoregulation play a critical role. However, the effect of osmotic pressure on single cell cycle dynamics remains poorly understood. Here, we investigate the effect of osmotic pressure on single cell cycle dynamics, nuclear growth, proliferation, migration and protein expression, by quantitative time-lapse imaging of single cells genetically modified with fluorescent ubiquitination-based cell cycle indicator 2 (FUCCI2). Single cell data reveals that under hyperosmotic stress, distinct cell subpopulations emerge with impaired nuclear growth, delayed or growth arrested cell cycle and reduced migration. This state is reversible for mild hyperosmotic stress, where cells return to regular cell cycle dynamics, proliferation and migration. Thus, osmotic pressure can modulate the reversible growth arrest and reactivation of human metastatic cells.

CellMAPtracer: A User-Friendly Tracking Tool for Long-Term Migratory and Proliferating Cells Associated with FUCCI Systems

Cell migration is a fundamental biological process of key importance in health and disease. Advances in imaging techniques have paved the way to monitor cell motility. An ever-growing collection of computational tools to track cells has improved our ability to analyze moving cells. One renowned goal in the field is to provide tools that track cell movement as comprehensively and automatically as possible. However, fully automated tracking over long intervals of time is challenged by dividing cells, thus calling for a combination of automated and supervised tracking. Furthermore, after the emergence of various experimental tools to monitor cell-cycle phases, it is of relevance to integrate the monitoring of cell-cycle phases and motility. We developed CellMAPtracer, a multiplatform tracking system that achieves that goal. It can be operated as a conventional, automated tracking tool of single cells in numerous imaging applications. However, CellMAPtracer also allows adjusting tracked cells in a semiautomated supervised fashion, thereby improving the accuracy and facilitating the long-term tracking of migratory and dividing cells. CellMAPtracer is available with a user-friendly graphical interface and does not require any coding or programming skills. CellMAPtracer is compatible with two- and three-color fluorescent ubiquitination-based cell-cycle indicator (FUCCI) systems and allows the user to accurately monitor various migration parameters throughout the cell cycle, thus having great potential to facilitate new discoveries in cell biology.

A novel evaluation method for Ki-67 immunostaining in paraffin-embedded tissues

The Ki-67 labeling index is traditionally used to investigate tumor aggressiveness. However, no diagnostic or prognostic value has been associated to the heterogeneous pattern of nuclear positivity. The aims of this study were to develop a classification for the patterns of Ki-67-positive nuclei; to search scientific evidence for the Ki-67 expression and location throughout the cell cycle; and to develop a protocol to apply the classification of patterns of Ki-67-positive nuclei in squamous epithelium with different proliferative activities. Based on empirical observation of paraffin sections submitted to immunohistochemistry for the determination of Ki-67 labeling index and literature review about Ki-67 expression, we created a classification of the patterns of nuclear positivity (NP1, NP2, NP3, NP4, and mitosis). A semi-automatic protocol was developed to identify and quantify the Ki-67 immunostaining patterns in target tissues. Two observers evaluated 7000 nuclei twice to test the intraobserver reliability, and six evaluated 1000 nuclei to the interobserver evaluation. The results showed that the immunohistochemical patterns of Ki-67 are similar in the tumoral and non-tumoral epithelium and were classified without difficulty. There was a high intraobserver reliability (Spearman correlation coefficient > 0.9) and moderate interobserver agreement (k = 0.523). Statistical analysis showed that non-malignant epithelial specimens presented a higher number of NP1 (geographic tongue = 83.8 ± 21.8; no lesion = 107.6 ± 52.7; and mild dysplasia = 86.6 ± 25.8) when compared to carcinoma in Situ (46.8 ± 34.8) and invasive carcinoma (72.6 ± 37.9). The statistical evaluation showed significant difference (p < 0.05). Thus, we propose a new way to evaluate Ki-67, where the pattern of its expression may be associated with the dynamics of the cell cycle. Future proof of this association will validate the use of the classification for its possible impact on cancer prognosis and guidance on personalized therapy.

Real-Time Fluorescence Image-Guided Oncolytic Virotherapy for Precise Cancer Treatment

Oncolytic virotherapy is one of the most promising, emerging cancer therapeutics. We generated three types of telomerase-specific replication-competent oncolytic adenovirus: OBP-301; a green fluorescent protein (GFP)-expressing adenovirus, OBP-401; and Killer-Red-armed OBP-301. These oncolytic adenoviruses are driven by the human telomerase reverse transcriptase (hTERT) promoter; therefore, they conditionally replicate preferentially in cancer cells. Fluorescence imaging enables visualization of invasion and metastasis in vivo at the subcellular level; including molecular dynamics of cancer cells, resulting in greater precision therapy. In the present review, we focused on fluorescence imaging applications to develop precision targeting for oncolytic virotherapy. Cell-cycle imaging with the fluorescence ubiquitination cell cycle indicator (FUCCI) demonstrated that combination therapy of an oncolytic adenovirus and a cytotoxic agent could precisely target quiescent, chemoresistant cancer stem cells (CSCs) based on decoying the cancer cells to cycle to S-phase by viral treatment, thereby rendering them chemosensitive. Non-invasive fluorescence imaging demonstrated that complete tumor resection with a precise margin, preservation of function, and prevention of distant metastasis, was achieved with fluorescence-guided surgery (FGS) with a GFP-reporter adenovirus. A combination of fluorescence imaging and laser ablation using a KillerRed-protein reporter adenovirus resulted in effective photodynamic cancer therapy (PDT). Thus, imaging technology and the designer oncolytic adenoviruses may have clinical potential for precise cancer targeting by indicating the optimal time for administering therapeutic agents; accurate surgical guidance for complete resection of tumors; and precise targeted cancer-specific photosensitization.