Haematopoietic ageing through the lens of single-cell technologies

ABSTRACTHuman lifespan is now longer than ever and, as a result, modern society is getting older. Despite that, the detailed mechanisms behind the ageing process and its impact on various tissues and organs remain obscure. In general, changes in DNA, RNA and protein structure throughout life impair their function. Haematopoietic ageing refers to the age-related changes affecting a haematopoietic system. Aged blood cells display different functional aberrations depending on their cell type, which might lead to the development of haematologic disorders, including leukaemias, anaemia or declining immunity. In contrast to traditional bulk assays, which are not suitable to dissect cell-to-cell variation, single-cell-level analysis provides unprecedented insight into the dynamics of age-associated changes in blood. In this Review, we summarise recent studies that dissect haematopoietic ageing at the single-cell level. We discuss what cellular changes occur during haematopoietic ageing at the genomic, transcriptomic, epigenomic and metabolomic level, and provide an overview of the benefits of investigating those changes with single-cell precision. We conclude by considering the potential clinical applications of single-cell techniques in geriatric haematology, focusing on the impact on haematopoietic stem cell transplantation in the elderly and infection studies, including recent COVID-19 research.

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A New Gene Set Identifies Senescent Cells and Predicts Senescence-Associated Pathways Across Tissues

10.21203/rs.3.rs-1034608/v1 ◽

2021 ◽

Author(s):

Sundeep Khosla ◽

Dominik Saul ◽

Robyn Laura Kosinsky ◽

Elizabeth Atkinson ◽

Madison Doolittle ◽

...

Keyword(s):

Single Cell ◽

Mesenchymal Cells ◽

Senescent Cell ◽

Single Cell Level ◽

Cell Level ◽

Gene Set ◽

Age Related ◽

Cell Clearance ◽

Bone Biopsies

Abstract Although cellular senescence is increasingly recognized as driving multiple age-related co-morbidities through the senescence-associated secretory phenotype (SASP), in vivo senescent cell identification, particularly in bulk or single cell RNA-sequencing (scRNA-seq) data remains challenging. Here, we generated a novel gene set (SenMayo) and first validated its enrichment in bone biopsies from two aged human cohorts. SenMayo also identified senescent cells in aged murine brain tissue, demonstrating applicability across tissues and species. For direct validation, we demonstrated significant reductions in SenMayo in bone following genetic clearance of senescent cells in mice, with similar findings in adipose tissue from humans in a pilot study of pharmacological senescent cell clearance. In direct comparisons, SenMayo outperformed all six existing senescence/SASP gene sets in identifying senescent cells across tissues and in demonstrating responses to senescent cell clearance. We next used SenMayo to identify senescent hematopoietic or mesenchymal cells at the single cell level from publicly available human and murine bone marrow/bone scRNA-seq data and identified monocytic and osteolineage cells, respectively, as showing the highest levels of senescence/SASP genes. Using pseudotime and cellular communication patterns, we found senescent hematopoietic and mesenchymal cells communicated with other cells through common pathways, including the Macrophage Migration Inhibitory Factor (MIF) pathway, which has been implicated not only in inflammation but also in immune evasion, an important property of senescent cells. Thus, SenMayo identifies senescent cells across tissues and species with high fidelity. Moreover, using this senescence panel, we were able to characterize senescent cells at the single cell level and identify key intercellular signaling pathways associated with these cells, which may be particularly useful for evolving efforts to map senescent cells (e.g., SenNet). In addition, SenMayo represents a potentially clinically applicable panel for monitoring senescent cell burden with aging and other conditions as well as in studies of senolytic drugs.

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Quantifying the efficacy of checkpoint inhibitors on CD8+ cytotoxic T cells for immunotherapeutic applications via single-cell interaction

Cell Death and Disease ◽

10.1038/s41419-020-03173-7 ◽

2020 ◽

Vol 11 (11) ◽

Author(s):

Matthew Ryan Sullivan ◽

Giovanni Stefano Ugolini ◽

Saheli Sarkar ◽

Wenjing Kang ◽

Evan Carlton Smith ◽

...

Keyword(s):

T Cells ◽

Combination Therapy ◽

Single Cell ◽

Checkpoint Inhibitors ◽

Cell Killing ◽

Calcium Flux ◽

Single Cell Level ◽

Cellular Interactions ◽

Cell Level ◽

The Impact

AbstractThe inhibition of the PD1/PDL1 pathway has led to remarkable clinical success for cancer treatment in some patients. Many, however, exhibit little to no response to this treatment. To increase the efficacy of PD1 inhibition, additional checkpoint inhibitors are being explored as combination therapy options. TSR-042 and TSR-033 are novel antibodies for the inhibition of the PD1 and LAG3 pathways, respectively, and are intended for combination therapy. Here, we explore the effect on cellular interactions of TSR-042 and TSR-033 alone and in combination at the single-cell level. Utilizing our droplet microfluidic platform, we use time-lapse microscopy to observe the effects of these antibodies on calcium flux in CD8+ T cells upon antigen presentation, as well as their effect on the cytotoxic potential of CD8+ T cells on human breast cancer cells. This platform allowed us to investigate the interactions between these treatments and their impacts on T-cell activity in greater detail than previously applied in vitro tests. The novel parameters we were able to observe included effects on the exact time to target cell killing, contact times, and potential for serial-killing by CD8+ T cells. We found that inhibition of LAG3 with TSR-033 resulted in a significant increase in calcium fluctuations of CD8+ T cells in contact with dendritic cells. We also found that the combination of TSR-042 and TSR-033 appears to synergistically increase tumor cell killing and the single-cell level. This study provides a novel single-cell-based assessment of the impact these checkpoint inhibitors have on cellular interactions with CD8+ T cells.

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Distinct Characteristics of Correlation Analysis at the Single-Cell and the Population Level

10.21203/rs.3.rs-42825/v1 ◽

2020 ◽

Author(s):

Guoyu Wu ◽

Yuchao Li

Keyword(s):

Correlation Analysis ◽

Single Cell ◽

Biological Networks ◽

Single Cells ◽

Correlation Coefficients ◽

Population Level ◽

Single Cell Level ◽

Cell Level ◽

Biological Studies ◽

The Impact

Abstract Background:Correlation analysis is widely used in biological studies to infer molecular relationships within biological networks. Recently, single-cell analysis has drawn tremendous interests, for its ability to obtain high-resolution molecular phenotypes. It turns out that there is little overlap of co-expressed genes identified in single-cell level investigations with that of population level investigations. However, the nature of the relationship of correlations between single-cell and population levels remains unclear. In this manuscript, we aimed to unveil the origin of the differences between the correlation coefficients at the single-cell level and that at the population level, and bridge the gap between them. Results:Through developing formulations to link correlations at the single-cell and the population level, we illustrated that aggregated correlations could be stronger, weaker or equal to the corresponding individual correlations, depending on the variations and the correlations within the population. When the correlation-within is weaker than the individual correlation, the correlation at the population level is stronger than that at the single-cell level. Through a bottom-up approach to model interactions between molecules in a signaling cascade or a multi-regulator controlled gene expression, we surprisingly found that the existence of interaction between two components could not be excluded simply based on their low correlation coefficients, suggesting a reconsideration of connectivity within biological networks which was derived solely from correlation analysis. We also investigated the impact of technical random measurement errors on the correlation coefficients for the single-cell level and the population level. The results indicate that the aggregated correlation is relatively robust and less affected.Conclusions:Because of the heterogeneity among single cells, correlation coefficients calculated based on data of the single-cell level might be different from that of the population level. Depending on the specific question we are asking, proper sampling and normalization procedure should be done before we draw any conclusions.

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A New Gene Set Identifies Senescent Cells and Predicts Senescence-Associated Pathways Across Tissues

10.1101/2021.12.10.472095 ◽

2021 ◽

Author(s):

Dominik Saul ◽

Robyn Laura Kosinsky ◽

Elizabeth J Atkinson ◽

Madison L. Doolittle ◽

Xu Zhang ◽

...

Keyword(s):

Single Cell ◽

Mesenchymal Cells ◽

Senescent Cell ◽

Single Cell Level ◽

Cell Level ◽

Gene Set ◽

Age Related ◽

Cell Clearance ◽

Bone Biopsies

AbstractAlthough cellular senescence is increasingly recognized as driving multiple age-related co-morbidities through the senescence-associated secretory phenotype (SASP), in vivo senescent cell identification, particularly in bulk or single cell RNA-sequencing (scRNA-seq) data remains challenging. Here, we generated a novel gene set (SenMayo) and first validated its enrichment in bone biopsies from two aged human cohorts. SenMayo also identified senescent cells in aged murine brain tissue, demonstrating applicability across tissues and species. For direct validation, we demonstrated significant reductions in SenMayo in bone following genetic clearance of senescent cells in mice, with similar findings in adipose tissue from humans in a pilot study of pharmacological senescent cell clearance. In direct comparisons, SenMayo outperformed all six existing senescence/SASP gene sets in identifying senescent cells across tissues and in demonstrating responses to senescent cell clearance. We next used SenMayo to identify senescent hematopoietic or mesenchymal cells at the single cell level from publicly available human and murine bone marrow/bone scRNA-seq data and identified monocytic and osteolineage cells, respectively, as showing the highest levels of senescence/SASP genes. Using pseudotime and cellular communication patterns, we found senescent hematopoietic and mesenchymal cells communicated with other cells through common pathways, including the Macrophage Migration Inhibitory Factor (MIF) pathway, which has been implicated not only in inflammation but also in immune evasion, an important property of senescent cells. Thus, SenMayo identifies senescent cells across tissues and species with high fidelity. Moreover, using this senescence panel, we were able to characterize senescent cells at the single cell level and identify key intercellular signaling pathways associated with these cells, which may be particularly useful for evolving efforts to map senescent cells (e.g., SenNet). In addition, SenMayo represents a potentially clinically applicable panel for monitoring senescent cell burden with aging and other conditions as well as in studies of senolytic drugs.

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Clonal dynamics of haematopoiesis across the human lifespan

10.1101/2021.08.16.456475 ◽

2021 ◽

Author(s):

Emily Mitchell ◽

Michael Spencer Chapman ◽

Nicholas Williams ◽

Kevin Dawson ◽

Nicole Mende ◽

...

Keyword(s):

Functional Decline ◽

Clonal Diversity ◽

Normal Subjects ◽

The Elderly ◽

Haematopoietic Stem Cell ◽

Driver Mutations ◽

Synonymous Mutations ◽

Human Lifespan ◽

Age Related ◽

Increased Risk

AbstractAge-related change in human haematopoiesis causes reduced regenerative capacity1, cytopenias2, immune dysfunction3 and increased risk of blood cancer. The cellular alterations that underpin the abruptness of this functional decline after the age of 70 years remain elusive. We sequenced 3579 genomes from single-cell-derived colonies of haematopoietic stem cell/multipotent progenitors (HSC/MPPs) across 10 haematologically normal subjects aged 0-81 years. HSC/MPPs accumulated 17 mutations/year after birth and lost 30bp/year of telomere length. Haematopoiesis in adults aged <65 was massively polyclonal, with high indices of clonal diversity and a stable population of 20,000–200,000 HSC/MPPs contributing evenly to blood production. In contrast, haematopoiesis in individuals aged >75 showed profoundly decreased clonal diversity. In each elderly subject, 30-60% of haematopoiesis was accounted for by 12-18 independent clones, each contributing 1-34% of blood production. Most clones had begun their expansion before age 40, but only 22% had known driver mutations. Genome-wide selection analysis estimated that 1/34 to 1/12 non-synonymous mutations were drivers, occurring at a constant rate throughout life, affecting a wider pool of genes than identified in blood cancers. Loss of Y chromosome conferred selective benefits on HSC/MPPs in males. Simulations from a simple model of haematopoiesis, with constant HSC population size and constant acquisition of driver mutations conferring moderate fitness benefits, entirely explained the abrupt change in clonal structure in the elderly. Rapidly decreasing clonal diversity is a universal feature of haematopoiesis in aged humans, underpinned by pervasive positive selection acting on many more genes than currently identified.

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