Transcriptomic Characterization of the Human Cell Cycle in Individual Unsynchronized Cells

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

Modification of Seurat v4 for the Development of a Phase Assignment Tool Able to Distinguish between G2 and Mitotic Cells

Single-cell RNA sequencing (scRNAseq) is a rapidly advancing field enabling the characterisation of heterogeneous gene expression profiles within a population. The cell cycle phase is a major contributor to gene expression variance between cells and computational analysis tools have been developed to assign cell cycle phases to cells within scRNAseq datasets. Whilst these tools can be extremely useful, all have the drawback that they classify cells as only G1, S or G2/M. Existing discrete cell phase assignment tools are unable to differentiate between G2 and M and continuous-phase-assignment tools are unable to identify a region corresponding specifically to mitosis in a pseudo-timeline for continuous assignment along the cell cycle. In this study, bulk RNA sequencing was used to identify differentially expressed genes between mitotic and interphase cells isolated based on phospho-histone H3 expression using fluorescence-activated cell sorting. These gene lists were used to develop a methodology which can distinguish G2 and M phase cells in scRNAseq datasets. The phase assignment tools present in Seurat were modified to allow for cell cycle phase assignment of all stages of the cell cycle to identify a mitotic-specific cell population.

FEED: a feature selection method based on gene expression decomposition for single cell clustering

Single-cell clustering is a critical step in biological downstream analysis. The clustering performance could be effectively improved by extracting cell-type-specific genes. The state-of-the-art feature selection methods usually calculate the importance of a single gene without considering the information contained in the gene expression distribution. Moreover, these methods ignore the intrinsic expression patterns of genes and heterogeneity within groups of different mean expression levels. In this work, we present a Feature sElection method based on gene Expression Decomposition (FEED) of scRNA-seq data, which selects informative genes to enhance clustering performance. First, the expression levels of genes are decomposed into multiple Gaussian components. Then, a novel gene correlation calculation method is proposed to measure the relationship between genes from the perspective of distribution. Finally, a permutation-based approach is proposed to determine the threshold of gene importance to obtain marker gene subsets. Compared with state-of-the-art feature selection methods, applying FEED on various scRNA-seq datasets including large datasets followed by different common clustering algorithms results in significant improvements in the accuracy of cell-type identification. The source codes for FEED are freely available at https://github.com/genemine/FEED.

Dynamics and regulation of mitotic chromatin accessibility bookmarking at single-cell resolution

Although mitotic chromosomes are highly compacted and transcriptionally inert, some active chromatin features are retained during mitosis to ensure the proper postmitotic reestablishment of maternal transcriptional programs, a phenomenon termed "mitotic bookmarking." However, the dynamics and regulation of mitotic bookmarking have not been systemically surveyed. Using single-cell transposase-accessible chromatin sequencing (scATAC-seq), we examined 6538 mitotic L02 human liver cells of variable stages and found that chromatin accessibility remained changing throughout cell division, with a constant decrease until metaphase and a gradual increase as chromosomes segregated. In particular, a subset of chromatin regions were identified to remain open throughout mitosis, and genes associated with these bookmarked regions are primarily linked to rapid reactivation upon mitotic exit. We also demonstrated that nuclear transcription factor Y subunit α (NF-YA) preferentially occupied bookmarked regions and contributed to transcriptional reactivation after mitosis. Our study uncovers the dynamic and regulatory blueprint of mitotic bookmarking.

Spathial: an R package for the evolutionary analysis of biological data

SUMMARY

A primary problem in high-throughput genomics experiments is finding the most important genes involved in biological processes (e.g. tumor progression). In this applications note, we introduce spathial, an R package for navigating high-dimensional data spaces. spathial implements the Principal Path algorithm, which is a topological method for locally navigating on the data manifold. The package, together with the core algorithm, provides several high-level functions for interpreting the results. One of the analyses we propose is the extraction of the genes that are mainly involved in the progress from one state to another. We show a possible application in the context of tumor progression using RNA-Seq and single-cell datasets, and we compare our results with two commonly used algorithms, edgeR and monocle3, respectively.

AVAILABILITY AND IMPLEMENTATION

The R package spathial is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/spathial/index.html) and on GitHub (https://github.com/erikagardini/spathial). It is distributed under the GNU General Public License (version 3).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Spatiotemporal dissection of the cell cycle with single-cell proteogenomics

The cell cycle, over which cells grow and divide, is a fundamental process of life. Its dysregulation has devastating consequences, including cancer^1-3. The cell cycle is driven by precise regulation of proteins in time and space, which creates variability between individual proliferating cells. To our knowledge, no systematic investigations of such cell-to-cell proteomic variability exist. Here we present a comprehensive, spatiotemporal map of human proteomic heterogeneity by integrating proteomics at subcellular resolution with single-cell transcriptomics and precise temporal measurements of individual cells in the cell cycle. We show that around one-fifth of the human proteome displays cell-to-cell variability, identify hundreds of proteins with previously unknown associations with mitosis and the cell cycle, and provide evidence that several of these proteins have oncogenic functions. Our results show that cell cycle progression explains less than half of all cell-to-cell variability, and that most cycling proteins are regulated post-translationally, rather than by transcriptomic cycling. These proteins are disproportionately phosphorylated by kinases that regulate cell fate, whereas non-cycling proteins that vary between cells are more likely to be modified by kinases that regulate metabolism. This spatially resolved proteomic map of the cell cycle is integrated into the Human Protein Atlas and will serve as a resource for accelerating molecular studies of the human cell cycle and cell proliferation.

DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

Total mRNA Quantification in Single Cells: Sarcoma Cell Heterogeneity

Single-cell analysis enables detailed molecular characterization of cells in relation to cell type, genotype, cell state, temporal variations, and microenvironment. These studies often include the analysis of individual genes and networks of genes. The total amount of RNA also varies between cells due to important factors, such as cell type, cell size, and cell cycle state. However, there is a lack of simple and sensitive methods to quantify the total amount of RNA, especially mRNA. Here, we developed a method to quantify total mRNA levels in single cells based on global reverse transcription followed by quantitative PCR. Standard curve analyses of diluted RNA and sorted cells showed a wide dynamic range, high reproducibility, and excellent sensitivity. Single-cell analysis of three sarcoma cell lines and human fibroblasts revealed cell type variations, a lognormal distribution of total mRNA levels, and up to an eight-fold difference in total mRNA levels among the cells. The approach can easily be combined with targeted or global gene expression profiling, providing new means to study cell heterogeneity at an individual gene level and at a global level. This method can be used to investigate the biological importance of variations in the total amount of mRNA in healthy as well as pathological conditions.

JAK-STAT signalling controls cancer stem cell properties including chemotherapy resistance in myxoid liposarcoma

Myxoid liposarcoma (MLS) shows extensive intratumoural heterogeneity with distinct subpopulations of tumour cells. Despite improved survival of MLS patients, existing therapies have shortcomings as they fail to target all tumour cells. The nature of chemotherapy‐resistant cells in MLS remains unknown. Here, we show that MLS cell lines contained subpopulations of cells that can form spheres, efflux Hoechst dye and resist doxorubicin, all properties attributed to cancer stem cells (CSCs). By single‐cell gene expression, western blot, phospho‐kinase array, immunoprecipitation, immunohistochemistry, flow cytometry and microarray analysis we showed that a subset of MLS cells expressed JAK–STAT genes with active signalling. JAK1/2 inhibition via ruxolitinib decreased, while stimulation with LIF increased, phosphorylation of STAT3 and the number of cells with CSC properties indicating that JAK–STAT signalling controlled the number of cells with CSC features. We also show that phosphorylated STAT3 interacted with the SWI/SNF complex. We conclude that MLS contains JAK–STAT‐regulated subpopulations of cells with CSC features. Combined doxorubicin and ruxolitinib treatment targeted both proliferating cells as well as cells with CSC features, providing new means to circumvent chemotherapy resistance in treatment of MLS patients.

What's new?

Despite improved survival of patients, existing therapies for Myxoid liposarcoma (MLS) present shortcomings as they fail to target all tumour cells. The nature of chemotherapy‐resistant cells in MLS remains unknown, however. Here, the authors show that myxoid liposarcomas are heterogeneous and contain subpopulations of cells with stem cell properties, including chemotherapy resistance. Moreover, JAK‐STAT signalling is active in MLS and regulates the size of the cancer stem cells‐like subpopulation via the SWI/SNF complex. The results shed light on the mechanisms of therapy resistance in MLS and point to JAK‐STAT inhibitors as a new avenue for targeted MLS therapies.

Identification of inhibitors regulating cell proliferation and FUS-DDIT3 expression in myxoid liposarcoma using combined DNA, mRNA, and protein analyses

FUS-DDIT3 belongs to the FET (FUS, EWSR1, and TAF15) family of fusion oncogenes, which collectively are considered to be key players in tumor development. Even though over 90% of all myxoid liposarcomas (MLS) have a FUS-DDIT3 gene fusion, there is limited understanding of the signaling pathways that regulate its expression. In order to study cell proliferation and FUS-DDIT3 regulation at mRNA and protein levels, we first developed a direct cell lysis approach that allows DNA, mRNA, and protein to be analyzed in the same sample using quantitative PCR, reverse transcription quantitative qPCR and proximity ligation assay, respectively. We screened 70 well-characterized kinase inhibitors and determined their effects on cell proliferation and expression of FUS-DDIT3 and FUS at both mRNA and protein levels in the MLS 402-91 cell line, where twelve selected inhibitors were evaluated further in two additional MLS cell lines. Both FUS-DDIT3 and FUS mRNA expression correlated with cell proliferation and both transcripts were co-regulated in most conditions, indicating that the common 5' FUS promotor is important in transcriptional regulation. In contrast, FUS-DDIT3 and FUS protein levels displayed more cell line dependent expression. Furthermore, most JAK inhibitors caused FUS-DDIT3 downregulation at both mRNA and protein levels. In conclusion, defining factors that regulate FUS-DDIT3 expression opens new means to understand MLS development at the molecular level.