Comparative Transcriptomic Analysis of the Hematopoietic System between Human and Mouse by Single Cell RNA Sequencing

Variation in mesenchymal KITL/SCF and IGF1 expression in middle age underlies steady-state hematopoietic stem cell aging

ABSTRACT

Intrinsic molecular programs and extrinsic factors including proinflammatory molecules are understood to regulate hematopoietic aging. This is based on foundational studies using genetic perturbation to evaluate causality. However, individual organisms exhibit natural variation in the hematopoietic aging phenotypes and the molecular basis of this heterogeneity is poorly understood. Here, we generated individual single-cell transcriptomic profiles of hematopoietic and nonhematopoietic cell types in 5 young adult and 9 middle-aged C57BL/6J female mice, providing a web-accessible transcriptomic resource for the field. Among all assessed cell types, hematopoietic stem cells (HSCs) exhibited the greatest phenotypic variation in expansion among individual middle-aged mice. We computationally pooled samples to define modules representing the molecular signatures of middle-aged HSCs and interrogated, which extrinsic regulatory cell types and factors would predict the variance in these signatures between individual middle-aged mice. Decline in signaling mediated by adiponectin, kit ligand (KITL) and insulin-like growth factor 1 (IGF1) from mesenchymal stromal cells (MSCs) was predicted to have the greatest transcriptional impact on middle-aged HSCs, as opposed to signaling mediated by endothelial cells or mature hematopoietic cell types. In individual middle-aged mice, lower expression of Kitl and Igf1 in MSCs was highly correlated with reduced lymphoid lineage commitment of HSCs and increased signatures of differentiation-inactive HSCs. These signatures were independent of expression of aging-associated proinflammatory cytokines including interleukin-1β (IL-1β), IL-6, tumor necrosis factor α and RANTES. In sum, we find that Kitl and Igf1 expression are coregulated and variable between individual mice at the middle age and expression of these factors is predictive of HSC activation and lymphoid commitment independently of inflammation.

The heterogeneity of erythroid cells: insight at the single-cell transcriptome level

Erythroid cells, the most prevalent cell type in blood, are one of the earliest products and permeate through the entire process of hematopoietic development in the human body, the oxygen-transporting function of which is crucial for maintaining overall health and life support. Previous investigations into erythrocyte differentiation and development have primarily focused on population-level analyses, lacking the single-cell perspective essential for comprehending the intricate pathways of erythroid maturation, differentiation, and the encompassing cellular heterogeneity. The continuous optimization of single-cell transcriptome sequencing technology, or single-cell RNA sequencing (scRNA-seq), provides a powerful tool for life sciences research, which has a particular superiority in the identification of unprecedented cell subgroups, the analyzing of cellular heterogeneity, and the transcriptomic characteristics of individual cells. Over the past decade, remarkable strides have been taken in the realm of single-cell RNA sequencing technology, profoundly enhancing our understanding of erythroid cells. In this review, we systematically summarize the recent developments in single-cell transcriptome sequencing technology and emphasize their substantial impact on the study of erythroid cells, highlighting their contributions, including the exploration of functional heterogeneity within erythroid populations, the identification of novel erythrocyte subgroups, the tracking of different erythroid lineages, and the unveiling of mechanisms governing erythroid fate decisions. These findings not only invigorate erythroid cell research but also offer new perspectives on the management of diseases related to erythroid cells.

Transcriptome network analysis of inflammation and fibrosis in keloids

BACKGROUND

Keloid (KL) is a common benign skin tumor. KL is typically characterized by significant fibrosis and an intensive inflammatory response. Therefore, a comprehensive understanding of the interactions between cellular inflammation and fibrotic cells is essential to elucidate the mechanisms driving the progression of KL and to develop therapeutics.

OBJECTIVE

Investigate the transcriptome landscape of inflammation and fibrosis in keloid scars.

METHODS

In this paper, we performed transcriptome sequencing and microRNA (miRNA) sequencing on unselected live cells from six human keloid tissues and normal skin tissues to elucidate a comprehensive transcriptome landscape. In addition, we used single-cell RNA sequencing (scRNA-seq) analysis to analyze intercellular communication networks and enrich fibroblast populations in two additional keloid and normal skin samples to study fibroblast diversity.

RESULTS

By RNA sequencing and a miRNA-mRNA-PPI network analysis, we identified miR-615-5p and miR-122b-3p as possible miRNAs associated with keloids, as they differed most significantly in keloids. Similarly, COL3A1, COL1A2, THBS2, TNC, IGTA, THBS4, TGFB3 as genes with significant differences in keloid may be associated with keloid development. Using single-cell RNA sequencing data from 24,086 cells collected from normal or keloid, we report reconstructed intercellular signaling network analysis and aggregation to modules associated with specific cell subpopulations at the cellular level for keloid alterations.

CONCLUSIONS

Our multitranscriptomic dataset delineates inflammatory and fibro heterogeneity of human keloids, underlining the importance of intercellular crosstalk between inflammatory cells and fibro cells and revealing potential therapeutic targets.

Single-Cell RNA Sequencing: Opportunities and Challenges for Studies on Corneal Biology in Health and Disease

The structure and major cell types of the multi-layer human cornea have been extensively studied. However, various cell states in specific cell types and key genes that define the cell states are not fully understood, hindering our comprehension of corneal homeostasis, related diseases, and therapeutic discovery. Single-cell RNA sequencing is a revolutionary and powerful tool for identifying cell states within tissues such as the cornea. This review provides an overview of current single-cell RNA sequencing studies on the human cornea, highlighting similarities and differences between them, and summarizing the key genes that define corneal cell states reported in these studies. In addition, this review discusses the opportunities and challenges of using single-cell RNA sequencing to study corneal biology in health and disease.

Dynamics of human hematopoietic stem and progenitor cell differentiation to the erythroid lineage

Erythropoiesis, the development of erythrocytes from hematopoietic stem cells, occurs through four phases: erythroid progenitor (EP) development, early erythropoiesis, terminal erythroid differentiation (TED), and maturation. According to the classical model that is based on immunophenotypic profiles of cell populations, each of these phases comprises multiple differentiation states that arise in a hierarchical manner. After segregation of lymphoid potential, erythroid priming begins during progenitor development and progresses through progenitor cell types that have multilineage potential. Complete separation of the erythroid lineage is achieved during early erythropoiesis with the formation of unipotent EPs: burst-forming unit-erythroid and colony-forming unit-erythroid. These erythroid-committed progenitors undergo TED and maturation, which involves expulsion of the nucleus and remodeling to form functional biconcave, hemoglobin-filled erythrocytes. In the last decade or so, many studies employing advanced techniques such as single-cell RNA-sequencing (scRNA-seq) as well as the conventional methods, including colony-forming cell assays and immunophenotyping, have revealed heterogeneity within the stem, progenitor, and erythroblast stages, and uncovered alternate paths for segregation of erythroid lineage potential. In this review, we provide an in-depth account of immunophenotypic profiles of all cell types within erythropoiesis, highlight studies that demonstrate heterogeneous erythroid stages, and describe deviations to the classical model of erythropoiesis. Overall, although scRNA-seq approaches have provided new insights, flow cytometry remains relevant and is the primary method for validation of novel immunophenotypes.

Single-Cell Analysis 2.0

In 1665, Robert Hooke published his revolutionary book Micrographia [...].

Cutting-Edge Methods for Better Understanding Cells

Cells are microscopic yet fundamental elements of life [...].

Time-Varying Gene Expression Network Analysis Reveals Conserved Transition States in Hematopoietic Differentiation between Human and Mouse

(1) Background: analyses of gene networks can elucidate hematopoietic differentiation from single-cell gene expression data, but most algorithms generate only a single, static network. Because gene interactions change over time, it is biologically meaningful to examine time-varying structures and to capture dynamic, even transient states, and cell-cell relationships. (2) Methods: a transcriptomic atlas of hematopoietic stem and progenitor cells was used for network analysis. After pseudo-time ordering with Monocle 2, LOGGLE was used to infer time-varying networks and to explore changes of differentiation gene networks over time. A range of network analysis tools were used to examine properties and genes in the inferred networks. (3) Results: shared characteristics of attributes during the evolution of differentiation gene networks showed a “U” shape of network density over time for all three branches for human and mouse. Differentiation appeared as a continuous process, originating from stem cells, through a brief transition state marked by fewer gene interactions, before stabilizing in a progenitor state. Human and mouse shared hub genes in evolutionary networks. (4) Conclusions: the conservation of network dynamics in the hematopoietic systems of mouse and human was reflected by shared hub genes and network topological changes during differentiation.