The Human Cell Atlas bone marrow single-cell interactive web portal

Alterations in the Transcriptional Programs of Myeloma Cells and the Microenvironment during Extramedullary Progression Affect Proliferation and Immune Evasion

PURPOSE

In multiple myeloma, extramedullary progression is associated with treatment resistance and a high mortality rate. To understand the molecular mechanisms controlling the devastating progression of myeloma, we applied single-cell RNA-sequencing (RNA-seq) to myeloma in the bone marrow and myelomatous pleural effusions or ascites.

EXPERIMENTAL DESIGN

Bone marrow or extramedullary myeloma samples were collected from 15 patients and subjected to single-cell RNA-seq. The single-cell transcriptome data of malignant plasma cells and the surrounding immune microenvironment were analyzed.

RESULTS

Comparisons of single-cell transcriptomes revealed the systematic activation of proliferation, antigen presentation, proteasomes, glycolysis, and oxidative phosphorylation pathways in extramedullary myeloma cells. The myeloma cells expressed multiple combinations of growth factors and receptors, suggesting autonomous and pleiotropic growth potential at the single-cell level. Comparisons of the tumor microenvironment revealed the presence of cytotoxic T lymphocytes and natural killer (NK) cells in both the bone marrow and extramedullary ascites, demonstrating a gene-expression phenotype indicative of functional compromise. In parallel, isolated myeloma cells persistently expressed class I MHC molecules and upregulated inhibitory molecules for cytotoxic T and NK cells.

CONCLUSIONS

These data suggest that myeloma cells are equipped with specialized immune evasion mechanisms in cytotoxic microenvironments. Taken together, single-cell transcriptome analysis revealed transcriptional programs associated with aggressive myeloma progression that support autonomous cell proliferation and immune evasion.

Single Cell Explorer, collaboration-driven tools to leverage large-scale single cell RNA-seq data

Background

Single cell transcriptome sequencing has become an increasingly valuable technology for dissecting complex biology at a resolution impossible with bulk sequencing. However, the gap between the technical expertise required to effectively work with the resultant high dimensional data and the biological expertise required to interpret the results in their biological context remains incompletely addressed by the currently available tools.

Results

Single Cell Explorer is a Python-based web server application we developed to enable computational and experimental scientists to iteratively and collaboratively annotate cell expression phenotypes within a user-friendly and visually appealing platform. These annotations can be modified and shared by multiple users to allow easy collaboration between computational scientists and experimental biologists. Data processing and analytic workflows can be integrated into the system using Jupyter notebooks. The application enables powerful yet accessible features such as the identification of differential gene expression patterns for user-defined cell populations and convenient annotation of cell types using marker genes or differential gene expression patterns. Users are able to produce plots without needing Python or R coding skills. As such, by making single cell RNA-seq data sharing and querying more user-friendly, the software promotes deeper understanding and innovation by research teams applying single cell transcriptomic approaches.

Conclusions

Single cell explorer is a freely-available single cell transcriptomic analysis tool that enables computational and experimental biologists to collaboratively explore, annotate, and share results in a flexible software environment and a centralized database server that supports data portal functionality.

Application of single-cell RNA sequencing methodologies in understanding haematopoiesis and immunology

The blood and immune system are characterised by utmost diversity in its cellular components. This heterogeneity can solely be resolved with the application of single-cell technologies that enable precise examination of cell-to-cell variation. Single-cell transcriptomics is continuously pushing forward our understanding of processes driving haematopoiesis and immune responses in physiological settings as well as in disease. Remarkably, in the last five years, a number of studies involving single-cell RNA sequencing (scRNA-seq) allowed the discovery of new immune cell types and revealed that haematopoiesis is a continuous rather than a stepwise process, thus challenging the classical haematopoietic lineage tree model. This review summarises the most recent studies which applied scRNA-seq to answer outstanding questions in the fields of haematology and immunology and discusses the present challenges and future directions.

A comprehensive single cell transcriptional landscape of human hematopoietic progenitors

Hematopoietic Stem/Progenitor cells (HSPCs) are endowed with the role of maintaining a diverse pool of blood cells throughout the human life. Despite recent efforts, the nature of the early cell fate decisions remains contentious. Using single-cell RNA-Seq, we show that existing approaches to stratify bone marrow CD34+ cells reveal a hierarchically-structured transcriptional landscape of hematopoietic differentiation. Still, this landscape misses important early fate decisions. We here provide a broader transcriptional profiling of bone marrow lineage negative hematopoietic progenitors that recovers a key missing branchpoint into basophils and expands our understanding of the underlying structure of early adult human haematopoiesis. We also show that this map has strong similarities in topology and gene expression to that found in mouse. Finally, we identify the sialomucin CD164, as a reliable marker for the earliest branches of HSPCs specification and we showed how its use can foster the design of alternative transplantation cell products.

Aging Human Hematopoietic Stem Cells Manifest Profound Epigenetic Reprogramming of Enhancers That May Predispose to Leukemia

Aging is associated with functional decline of hematopoietic stem cells (HSC) as well as an increased risk of myeloid malignancies. We performed an integrative characterization of epigenomic and transcriptomic changes, including single-cell RNA sequencing, during normal human aging. Lineage^-CD34⁺CD38^- cells [HSC-enriched (HSCe)] undergo age-associated epigenetic reprogramming consisting of redistribution of DNA methylation and reductions in H3K27ac, H3K4me1, and H3K4me3. This reprogramming of aged HSCe globally targets developmental and cancer pathways that are comparably altered in acute myeloid leukemia (AML) of all ages, encompassing loss of 4,646 active enhancers, 3,091 bivalent promoters, and deregulation of several epigenetic modifiers and key hematopoietic transcription factors, such as KLF6, BCL6, and RUNX3. Notably, in vitro downregulation of KLF6 results in impaired differentiation, increased colony-forming potential, and changes in expression that recapitulate aging and leukemia signatures. Thus, age-associated epigenetic reprogramming may form a predisposing condition for the development of age-related AML. SIGNIFICANCE: AML, which is more frequent in the elderly, is characterized by epigenetic deregulation. We demonstrate that epigenetic reprogramming of human HSCs occurs with age, affecting cancer and developmental pathways. Downregulation of genes epigenetically altered with age leads to impairment in differentiation and partially recapitulates aging phenotypes.This article is highlighted in the In This Issue feature, p. 983.

The Pediatric Cell Atlas: Defining the Growth Phase of Human Development at Single-Cell Resolution

Single-cell gene expression analyses of mammalian tissues have uncovered profound stage-specific molecular regulatory phenomena that have changed the understanding of unique cell types and signaling pathways critical for lineage determination, morphogenesis, and growth. We discuss here the case for a Pediatric Cell Atlas as part of the Human Cell Atlas consortium to provide single-cell profiles and spatial characterization of gene expression across human tissues and organs. Such data will complement adult and developmentally focused HCA projects to provide a rich cytogenomic framework for understanding not only pediatric health and disease but also environmental and genetic impacts across the human lifespan.

New insights into hematopoietic differentiation landscapes from single-cell RNA sequencing

Single-cell transcriptomics has recently emerged as a powerful tool to analyze cellular heterogeneity, discover new cell types, and infer putative differentiation routes. The technique has been rapidly embraced by the hematopoiesis research community, and like other technologies before, single-cell molecular profiling is widely expected to make important contributions to our understanding of the hematopoietic hierarchy. Much of this new interpretation relies on inference of the transcriptomic landscape as a representation of existing cellular states and associated transitions among them. Here we review how this model allows, under certain assumptions, charting of time-resolved differentiation trajectories with unparalleled resolution and how the landscape of multipotent cells may be rather devoid of discrete structures, challenging our preconceptions about stem and progenitor cell types and their organization. Finally, we highlight how promising technological advances may convert static differentiation landscapes into a dynamic cell flux model and thus provide a more holistic understanding of normal hematopoiesis and blood disorders.

Single-Cell RNA-Seq Reveals AML Hierarchies Relevant to Disease Progression and Immunity

Acute myeloid leukemia (AML) is a heterogeneous disease that resides within a complex microenvironment, complicating efforts to understand how different cell types contribute to disease progression. We combined single-cell RNA sequencing and genotyping to profile 38,410 cells from 40 bone marrow aspirates, including 16 AML patients and five healthy donors. We then applied a machine learning classifier to distinguish a spectrum of malignant cell types whose abundances varied between patients and between subclones in the same tumor. Cell type compositions correlated with prototypic genetic lesions, including an association of FLT3-ITD with abundant progenitor-like cells. Primitive AML cells exhibited dysregulated transcriptional programs with co-expression of stemness and myeloid priming genes and had prognostic significance. Differentiated monocyte-like AML cells expressed diverse immunomodulatory genes and suppressed T cell activity in vitro. In conclusion, we provide single-cell technologies and an atlas of AML cell states, regulators, and markers with implications for precision medicine and immune therapies. VIDEO ABSTRACT.