Dissecting stem cell differentiation using single cell expression profiling

Temporal Gene Expression Profiles Reflect the Dynamics of Lymphoid Differentiation

Understanding the emergence of lymphoid committed cells from multipotent progenitors (MPP) is a great challenge in hematopoiesis. To gain deeper insight into the dynamic expression changes associated with these transitions, we report the quantitative transcriptome of two MPP subsets and the common lymphoid progenitor (CLP). While the transcriptome is rather stable between MPP2 and MPP3, expression changes increase with differentiation. Among those, we found that pioneer lymphoid genes such as Rag1, Mpeg1, and Dntt are expressed continuously from MPP2. Others, such as CD93, are CLP specific, suggesting their potential use as new markers to improve purification of lymphoid populations. Notably, a six-transcription factor network orchestrates the lymphoid differentiation program. Additionally, we pinpointed 24 long intergenic-non-coding RNA (lincRNA) differentially expressed through commitment and further identified seven novel forms. Collectively, our approach provides a comprehensive landscape of coding and non-coding transcriptomes expressed during lymphoid commitment.

Between Fate Choice and Self-Renewal-Heterogeneity of Adult Neural Crest-Derived Stem Cells

Stem cells of the neural crest (NC) vitally participate to embryonic development, but also remain in distinct niches as quiescent neural crest-derived stem cell (NCSC) pools into adulthood. Although NCSC-populations share a high capacity for self-renewal and differentiation resulting in promising preclinical applications within the last two decades, inter- and intrapopulational differences exist in terms of their expression signatures and regenerative capability. Differentiation and self-renewal of stem cells in developmental and regenerative contexts are partially regulated by the niche or culture condition and further influenced by single cell decision processes, making cell-to-cell variation and heterogeneity critical for understanding adult stem cell populations. The present review summarizes current knowledge of the cellular heterogeneity within NCSC-populations located in distinct craniofacial and trunk niches including the nasal cavity, olfactory bulb, oral tissues or skin. We shed light on the impact of intrapopulational heterogeneity on fate specifications and plasticity of NCSCs in their niches in vivo as well as during in vitro culture. We further discuss underlying molecular regulators determining fate specifications of NCSCs, suggesting a regulatory network including NF-κB and NC-related transcription factors like SLUG and SOX9 accompanied by Wnt- and MAPK-signaling to orchestrate NCSC stemness and differentiation. In summary, adult NCSCs show a broad heterogeneity on the level of the donor and the donors' sex, the cell population and the single stem cell directly impacting their differentiation capability and fate choices in vivo and in vitro. The findings discussed here emphasize heterogeneity of NCSCs as a crucial parameter for understanding their role in tissue homeostasis and regeneration and for improving their applicability in regenerative medicine.

GMM-Demux: sample demultiplexing, multiplet detection, experiment planning, and novel cell-type verification in single cell sequencing

Identifying and removing multiplets are essential to improving the scalability and the reliability of single cell RNA sequencing (scRNA-seq). Multiplets create artificial cell types in the dataset. We propose a Gaussian mixture model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes multiplets through sample barcoding, including cell hashing and MULTI-seq. GMM-Demux uses a droplet formation model to authenticate putative cell types discovered from a scRNA-seq dataset. We generate two in-house cell-hashing datasets and compared GMM-Demux against three state-of-the-art sample barcoding classifiers. We show that GMM-Demux is stable and highly accurate and recognizes 9 multiplet-induced fake cell types in a PBMC dataset.

FGF9 induces functional differentiation to Schwann cells from human adipose derived stem cells

Rationale: The formation of adipose-derived stem cells (ASCs) into spheres on a chitosan-coated microenvironment promoted ASCs differentiation into a mixed population of neural lineage-like cells (NLCs), but the underline mechanism is still unknown. Since the fibroblast growth factor 9 (FGF9) and fibroblast growth factor receptors (FGFRs) play as key regulators of neural cell fate during embryo development and stem cell differentiation, the current study aims to reveal the interplay of FGF9 and FGFRs for promoting peripheral nerve regeneration.

Methods: Different concentration of FGF9 peptide (10, 25, 50, 100 ng/mL) were added during NLCs induction (FGF9-NLCs). The FGFR expressions and potential signaling were studied by gene and protein expressions as well as knocking down by specific FGFR siRNA or commercial inhibitors. FGF9-NLCs were fluorescent labeled and applied into a nerve conduit upon the injured sciatic nerves of experimental rats.

Results: The FGFR2 and FGFR4 were significantly increased during NLCs induction. The FGF9 treated FGF9-NLCs spheres became smaller and changed into Schwann cells (SCs) which expressed S100β and GFAP. The specific silencing of FGFR2 diminished FGF9-induced Akt phosphorylation and inhibited the differentiation of SCs. Transplanted FGF9-NLCs participated in myelin sheath formation, enhanced axonal regrowth and promoted innervated muscle regeneration. The knockdown of FGFR2 in FGF9-NLCs led to the abolishment of nerve regeneration.

Conclusions: Our data therefore demonstrate the importance of FGF9 in the determination of SC fate via the FGF9-FGFR2-Akt pathway and reveal the therapeutic benefit of FGF9-NLCs.

Revealing Dynamic Mechanisms of Cell Fate Decisions From Single-Cell Transcriptomic Data

Cell fate decisions play a pivotal role in development, but technologies for dissecting them are limited. We developed a multifunction new method, Topographer, to construct a "quantitative" Waddington's landscape of single-cell transcriptomic data. This method is able to identify complex cell-state transition trajectories and to estimate complex cell-type dynamics characterized by fate and transition probabilities. It also infers both marker gene networks and their dynamic changes as well as dynamic characteristics of transcriptional bursting along the cell-state transition trajectories. Applying this method to single-cell RNA-seq data on the differentiation of primary human myoblasts, we not only identified three known cell types, but also estimated both their fate probabilities and transition probabilities among them. We found that the percent of genes expressed in a bursty manner is significantly higher at (or near) the branch point (~97%) than before or after branch (below 80%), and that both gene-gene and cell-cell correlation degrees are apparently lower near the branch point than away from the branching. Topographer allows revealing of cell fate mechanisms in a coherent way at three scales: cell lineage (macroscopic), gene network (mesoscopic), and gene expression (microscopic).

Gene Regulatory Networks from Single Cell Data for Exploring Cell Fate Decisions

Single cell experimental techniques now allow us to quantify gene expression in up to thousands of individual cells. These data reveal the changes in transcriptional state that occur as cells progress through development and adopt specialized cell fates. In this chapter we describe in detail how to use our network inference algorithm (PIDC)-and the associated software package NetworkInference.jl-to infer functional interactions between genes from the observed gene expression patterns. We exploit the large sample sizes and inherent variability of single cell data to detect statistical dependencies between genes that indicate putative (co-)regulatory relationships, using multivariate information measures that can capture complex statistical relationships. We provide guidelines on how best to combine this analysis with other complementary methods designed to explore single cell data, and how to interpret the resulting gene regulatory network models to gain insight into the processes regulating cell differentiation.

Differentiation-based model of hematopoietic stem cell functions and lineage pathways

Advances in genetic labeling and barcoding of hematopoietic stem cells (HSCs) in situ now allow direct measurements of physiological HSC output, both quantitatively and qualitatively. Turning on a heritable label in HSCs and measuring the kinetics of label emergence in downstream compartments reveal rates of differentiation and self-renewal of HSCs and progenitor cells, whereas endogenous HSC barcoding probes physiological precursor-product relationships. Labels have been inserted at different stages of the hematopoietic differentiation hierarchy. Recent genetic and functional evidence suggests a phenotype (Tie2⁺ ) for tip HSCs. Fate mapping shows that many tip HSCs regularly feed into downstream stages, with individual cells contributing infrequently. Stem and progenitor cells downstream of tip HSCs serve as a major, nearly self-renewing source of day-to-day hematopoiesis, rendering the blood and immune system HSC-independent for extended periods of time. HSCs realize multilineage output, yet, fates restricted to several lineages or even a single lineage have also been observed. Single HSCs within a clone in the bone marrow that develop from a fetal HSC precursor have been observed to express clone-specific fates. Thus, the new tools probing HSC differentiation in situ are progressing beyond assays for HSC activity based on proliferation measurements and fates of transplanted stem cells, and the data challenge lineage interpretations of single-cell gene expression snapshots. Linking in vivo fate analyses to gene expression and other molecular determinants of cell fate will aid in unraveling the mechanisms of lineage commitment and the architecture of physiological hematopoiesis.

Single-Cell Sequencing in Normal and Malignant Hematopoiesis

Hematopoiesis is one of the best studied adult stem-cell systems, with a differentiation hierarchy progressing from immature hematopoietic stem cells to over 10 distinct mature cell types. Recent technological breakthroughs now make it possible to define transcriptional profiles in thousands of individual cells. Facilitated by the wealth of prior data on cell purification and analysis strategies, hematopoiesis has been one of the earliest experimental systems to which many of these new single-cell sequencing technologies have been applied. In this review, the authors focus on recent studies, which have shed light on heterogeneity within individual populations as well as the relationships between populations, and also attempt to characterize the differences between normal and disease/perturbed states.

Mapping the CLEC12A expression on myeloid progenitors in normal bone marrow; implications for understanding CLEC12A-related cancer stem cell biology

The C-type lectin domain family 12, member A (CLEC12A) receptor has emerged as a leukaemia-associated and cancer stem cell marker in myeloid malignancies. However, a detailed delineation of its expression in normal haematopoiesis is lacking. Here, we have characterized the expression pattern of CLEC12A on the earliest stem- and myeloid progenitor subsets in normal bone marrow. We demonstrate distinct CLEC12A expression in the classically defined myeloid progenitors, where on average 39.1% (95% CI [32.5;45.7]) of the common myeloid progenitors (CMPs) expressed CLEC12A, while for granulocyte-macrophage progenitors and megakaryocyte-erythroid progenitors (MEPs), the average percentages were 81.0% (95% CI [76.0;85.9]) and 11.9% (95% CI [9.3;14.6]), respectively. In line with the reduced CLEC12A expression on MEPs, functional assessment of purified CLEC12A^+/- CMPs and MEPs in the colony-forming unit assay demonstrated CLEC12A⁺ subsets to favour non-erythroid colony growth. In conclusion, we provide evidence that the earliest CLEC12A⁺ cell in the haematopoietic tree is the classically defined CMP. Furthermore, we show that CLEC12A-expressing CMPs and MEPs are functionally different than their negative counterparts. Importantly, these data can help determine which cells will be spared during CLEC12A-targeted therapy, and we propose CLEC12A to be included in future studies of myeloid cancer stem cell biology.

Lineage specification of early embryos and embryonic stem cells at the dawn of enabling technologies

Establishment of progenitor cell populations and lineage diversity during embryogenesis and the differentiation of pluripotent stem cells is a fascinating and intricate biological process. Conceptually, an understanding of this developmental process provides a framework to integrate stem-cell pluripotency, cell competence and differentiating potential with the activity of extrinsic and intrinsic molecular determinants. The recent advent of enabling technologies of high-resolution transcriptome analysis at the cellular, population and spatial levels proffers the capability of gaining deeper insights into the attributes of the gene regulatory network and molecular signaling in lineage specification and differentiation. In this review, we provide a snapshot of the emerging enabling genomic technologies that contribute to the study of development and stem-cell biology.