Single-Cell RNA-Sequencing Identifies Activation of TP53 and STAT1 Pathways in Human T Lymphocyte Subpopulations in Response to Ex Vivo Radiation Exposure

Detrimental health consequences from exposure to space radiation are a major concern for long-duration human exploration missions to the Moon or Mars. Cellular responses to radiation are expected to be heterogeneous for space radiation exposure, where only high-energy protons and other particles traverse a fraction of the cells. Therefore, assessing DNA damage and DNA damage response in individual cells is crucial in understanding the mechanisms by which cells respond to different particle types and energies in space. In this project, we identified a cell-specific signature for radiation response by using single-cell transcriptomics of human lymphocyte subpopulations. We investigated gene expression in individual human T lymphocytes 3 h after ex vivo exposure to 2-Gy gamma rays while using the single-cell sequencing technique (10X Genomics). In the process, RNA was isolated from ~700 irradiated and ~700 non-irradiated control cells, and then sequenced with ~50 k reads/cell. RNA in each of the cells was distinctively barcoded prior to extraction to allow for quantification for individual cells. Principal component and clustering analysis of the unique molecular identifier (UMI) counts classified the cells into three groups or sub-types, which correspond to CD4+, naïve, and CD8+/NK cells. Gene expression changes after radiation exposure were evaluated using negative binomial regression. On average, BBC3, PCNA, and other TP53 related genes that are known to respond to radiation in human T cells showed increased activation. While most of the TP53 responsive genes were upregulated in all groups of cells, the expressions of IRF1, STAT1, and BATF were only upregulated in the CD4+ and naïve groups, but were unchanged in the CD8+/NK group, which suggests that the interferon-gamma pathway does not respond to radiation in CD8+/NK cells. Thus, single-cell RNA sequencing technique was useful for simultaneously identifying the expression of a set of genes in individual cells and T lymphocyte subpopulation after gamma radiation exposure. The degree of dependence of UMI counts between pairs of upregulated genes was also evaluated to construct a similarity matrix for cluster analysis. The cluster analysis identified a group of TP53-responsive genes and a group of genes that are involved in the interferon gamma pathway, which demonstrate the potential of this method for identifying previously unknown groups of genes with similar expression patterns.

Download Full-text

Refining the adipose progenitor cell landscape in healthy and obese visceral adipose tissue using single-cell gene expression profiling

Life Science Alliance ◽

10.26508/lsa.201900561 ◽

2019 ◽

Vol 2 (6) ◽

pp. e201900561 ◽

Cited By ~ 9

Author(s):

Dong Seong Cho ◽

Bolim Lee ◽

Jason D Doles

Keyword(s):

Gene Expression ◽

Adipose Tissue ◽

Single Cell ◽

Rna Sequencing ◽

Visceral Adipose Tissue ◽

Ex Vivo ◽

Cellular Heterogeneity ◽

Health Concern ◽

Single Cell Rna Sequencing ◽

Visceral Adipose

Obesity is a serious health concern and is associated with a reduced quality of life and a number of chronic diseases, including diabetes, heart disease, stroke, and cancer. With obesity rates on the rise worldwide, adipose tissue biology has become a top biomedical research priority. Despite steady growth in obesity-related research, more investigation into the basic biology of adipose tissue is needed to drive innovative solutions aiming to curtail the obesity epidemic. Adipose progenitor cells (APCs) play a central role in adipose tissue homeostasis and coordinate adipose tissue expansion and remodeling. Although APCs are well studied, defining and characterizing APC subsets remains ambiguous because of ill-defined cellular heterogeneity within this cellular compartment. In this study, we used single-cell RNA sequencing to create a cellular atlas of APC heterogeneity in mouse visceral adipose tissue. Our analysis identified two distinct populations of adipose tissue–derived stem cells (ASCs) and three distinct populations of preadipocytes (PAs). We identified novel cell surface markers that, when used in combination with traditional ASC and preadipocyte markers, could discriminate between these APC subpopulations by flow cytometry. Prospective isolation and molecular characterization of these APC subpopulations confirmed single-cell RNA sequencing gene expression signatures, and ex vivo culture revealed differential expansion/differentiation capabilities. Obese visceral adipose tissue featured relative expansion of less mature ASC and PA subpopulations, and expression analyses revealed major obesity-associated signaling alterations within each APC subpopulation. Taken together, our study highlights cellular and transcriptional heterogeneity within the APC pool, provides new tools to prospectively isolate and study these novel subpopulations, and underscores the importance of considering APC diversity when studying the etiology of obesity.

Download Full-text

Single Cell RNA Sequencing Identifies Potential Molecular Indicators of Response to Melflufen in Multiple Myeloma

Blood ◽

10.1182/blood-2021-147191 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 1194-1194

Author(s):

Philipp Sergeev ◽

Sadiksha Adhikari ◽

Juho J. Miettinen ◽

Maiju-Emilia Huppunen ◽

Minna Suvela ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Single Cell ◽

Research Funding ◽

Drug Sensitivity ◽

Ex Vivo ◽

Cell Populations ◽

Drug Efflux ◽

Single Cell Rna Sequencing ◽

Differential Gene

Abstract Introduction Melphalan flufenamide (melflufen), is a novel peptide-drug conjugate that targets aminopeptidases and selectively delivers alkylating agents in tumors. Melflufen was recently FDA approved for the treatment of relapsed/refractory multiple myeloma (MM) patients. Considering the challenges in treating this group of patients, and the availability of several new drugs for MM, information that can support treatment selection is urgently needed. To identify potential indicators of response and mechanism of resistance to melflufen, we applied a multiparametric drug sensitivity assay to MM patient samples ex vivo and analyzed the samples by single cell RNA sequencing (scRNAseq). Ex vivo drug testing identified MM samples that were distinctly sensitive or resistant to melflufen, while differential gene expression analysis revealed pathways associated with response. Methods Bone marrow (BM) aspirates from 24 MM patients were obtained after written informed consent following approved protocols in compliance with the Declaration of Helsinki. BM mononuclear cells from 12 newly diagnosed (ND) and 12 relapsed/refractory (RR) patients were used for multi-parametric flow cytometry-based drug sensitivity and resistance testing (DSRT) evaluation to melflufen and melphalan, and for scRNAseq. Based on the results from the DSRT tests and drug sensitivity scores (DSS), we divided the samples into three groups - high sensitivity (HS, DSS > 40 (melflufen) or DSS > 16 (melphalan)), intermediate sensitivity (IS, 31 ≤ DSS ≤ 40 (melflufen) or 10 ≤ DSS ≤ 16 (melphalan)), and low sensitivity (LS, DSS < 31 (melflufen) or DSS < 10 (melphalan)). To identify genes, responsible for the general sensitivity to melphalan-based drugs we conducted differential gene expression (DGE) analyses separately for melphalan and melflufen focusing on the plasma cell populations, comparing gene expression between HS and LS samples for both drugs ("HS vs. LS melphalan" and "HS vs. LS for melflufen", respectively). In addition, to explain the increased sensitivity of RR samples, we conducted the DGE analysis for ND vs. RR samples and searched for similarities between these three datasets. Results DSRT data indicated that samples from RRMM patients were significantly more sensitive to melflufen compared to samples from NDMM (Fig. 1A). In addition, we observed that samples with a gain of 1q (+1q) were more sensitive to melflufen while those with deletion of 13q (del13q) appeared to be less sensitive, although these results lacked significance (Fig. 1A). After separating the samples into different drug sensitivity groups (HS, IS, LS), DGE analysis showed significant downregulation of the drug efflux and multidrug resistance protein family member ABCB9 in the melflufen HS group opposed to the LS group (2.2-fold, p < 0.001). A similar pattern was detected for the melphalan HS vs. LS comparison suggesting that this alteration might be a common indicator of sensitivity to melphalan-based drugs. Furthermore, in the melflufen HS group we observed downregulation of the matrix metallopeptidase inhibitors TIMP1 and TIMP2 (3-fold and 1.6-fold, p < 0.001, respectively), and cathepsin inhibitors CST3 and CSTB (3.2-fold and 1.3-fold, p < 0.001, respectively) (Fig. 1B). This effect was observed in both "ND vs. RR" and "HS vs. LS for melflufen" comparisons, but not for melphalan, suggesting that these changes are associated with disease progression and specific indicators of sensitivity to melflufen. Moreover, gene set enrichment analysis (GSEA) showed activation of pathways related to protein synthesis, as well as amino acid starvation for malignant and normal cell populations in the HS group. Conclusion In summary, our results indicate that melflufen is more active in RRMM compared to NDMM. In addition, samples from MM patients with +1q, which is considered an indicator of high-risk disease, tended to be more sensitive to melflufen. Based on differential GSEA and pathway enrichment, several synergizing mechanisms could potentially explain the higher sensitivity to melflufen, such as decreased drug efflux and increased drug uptake. Although these results indicate potential indicators of response and mechanisms of drug efficacy, further validation of these findings is required using data from melflufen treated patients. Figure 1 Figure 1. Disclosures Slipicevic: Oncopeptides AB: Current Employment. Nupponen: Oncopeptides AB: Consultancy. Lehmann: Oncopeptides AB: Current Employment. Heckman: Orion Pharma: Research Funding; Oncopeptides: Consultancy, Research Funding; Novartis: Research Funding; Celgene/BMS: Research Funding; Kronos Bio, Inc.: Research Funding.

Download Full-text

Dissecting Ex Vivo Expansion of Mobilized Peripheral Blood Hematopoietic Stem and Progenitor Cells By Single Cell RNA Sequencing

Blood ◽

10.1182/blood-2018-99-111879 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 3343-3343

Author(s):

Matteo Maria Naldini ◽

Gabriele Casirati ◽

Erika Zonari ◽

Giacomo Desantis ◽

Andrea Cammarata ◽

...

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Gene Therapy ◽

Single Cell ◽

Progenitor Cells ◽

Peripheral Blood ◽

Ex Vivo ◽

Hematopoietic Stem ◽

Single Cell Rna Sequencing ◽

Mobilized Peripheral Blood

Abstract Hematopoietic stem and progenitor cell (HSPC) expansion remains an important unmet goal for ex vivo gene therapy based on gene addition and editing to compensate for the negative impact of the gene transfer procedure enabling faster engraftment and less complications. Additionally, ex vivo expansion of corrected cells may improve efficacy at more sustainable manufacturing costs by downscaling transduction. To date, our knowledge of precise mechanisms of action of expansion compounds is limited, and it remains unclear whether cord blood expansion protocols also maintain stemness of mobilized peripheral blood CD34+ cells (mPB), the preferred HSPC source for gene therapy. We performed serial (day 0,4,8) droplet-based single cell RNA sequencing (scRNAseq) on lentivirally transduced mPB expanded with UM171 to dissect cellular heterogeneity, monitor population dynamics over time and identify a transcriptional profile of primitive cells in culture. By associating published HSPC gene expression profiles to our scRNAseq dataset from uncultured mPB, we found that 45% of cells harbored a myelo-lymphoid signature. Smaller cell clusters expressed a shared erythroid (ERY) and megakaryocytic (MK) signature (20%), or a more primitive multipotent HSC-like signature (15%) characterized by enhanced JAK/STAT signaling and expression of HSC associated genes (AVP, HOPX, ID3). Unsupervised ordering of cells within pseudotime separated emerging MK/ERYpoiesis (FCER1A, HBD) from lympho-myelopoiesis (CD52, JUN), with intermediate states of more primitive progenitors located in between. After 4 days in culture, we noted a general increase in nuclear and mitochondrial gene transcription with activation of oxidative metabolism, paralleled by cell cycle activation, as expected from cytokine stimulation. By d8 of culture these changes leveled off but remained higher than uncultured cells. Of note, cells at d8 revealed an activation of cellular stress response pathways (e.g. TNFa, IFNg) hinting towards a compromised culture that may eventually exhaust HSC. Unsupervised clustering of cultured mPB highlighted a dramatic expansion (70-80%) of MK/ERY progenitor cells with high cycling activity with only 20-30% cells showing myelo-lymphoid transcriptional features. In line, pseudotime analysis highlighted a main ERY and MK trajectory separated from that of cells characterized by the expression of HSPC genes (HOPX, SPINK2) and of an emerging myeloid trajectory (MPO). To profile HSC in culture, we sorted and sequenced CD34+90+201+ cells from d4 expansion culture (3% of total cells), which we show to contain >70% of SCID repopulating potential. ScRNAseq revealed transcriptional similarity with the myelo-lymphoid progenitor cluster identified in the unsorted d4 culture. Unsupervised clustering of the CD34+90+201+ population revealed cell cycle dependent heterogeneity, identifying a highly quiescent cluster with expression of HSC-like signatures. This cluster was also characterized by relatively low gene expression, possibly reflecting a non-activated cell state consistent with primitive HSPC. Pseudotime analysis produced a four-branched minimum spanning tree, which retained a clear cell cycle and metabolic effect. Top variable genes included cell cycle, glycolytic, mitochondrial and ribosomal genes, identifying different metabolic modules along the branched trajectory. These results highlight that cell heterogeneity within a purified, HSC-enriched population is driven mainly by metabolic activation and cell cycle status. As a complementary approach, we purified LT-HSC from uncultured mPB (CD34+38-90+45RA-49f+), marked them with CFSE and expanded them in UM171 culture. LT-HSCs expanded on average 3.5 fold in 7 days, with the following distribution: 0 divisions: 3%; 1: 26%; 2: 47%; 3: 21%; 4: 3%. We performed scRNAseq on LT-HSC pre culture and after 7d separating a highly proliferative (≥2 divisions) and quiescent (0 - 1 division) fraction, allowing us to obtain unprecedented insight into the response of engrafting cells to ex vivo culture and set a framework to dissect self-renewal (HSC expansion), HSC maintenance and loss through differentiation as potential culture outcomes. Our combined functional/transcriptomic approach will define new HSC markers in culture and greatly facilitate side-by-side comparison of different expansion protocols towards rapid clinical translation. Disclosures No relevant conflicts of interest to declare.

Download Full-text

Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through ‘reverse phenotyping’

Nature Communications ◽

10.1038/s41467-021-24730-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

David S. Fischer ◽

Meshal Ansari ◽

Karolin I. Wagner ◽

Sebastian Jarosch ◽

Yiqi Huang ◽

...

Keyword(s):

T Cells ◽

Single Cell ◽

Rna Sequencing ◽

Ex Vivo ◽

Severe Disease ◽

Human Leukocyte ◽

Cell Receptor ◽

Single Cell Rna Sequencing

AbstractThe in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we use single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for ‘reverse phenotyping’. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states.

Download Full-text

The order and logic of CD4 versus CD8 lineage choice and differentiation in mouse thymus

Nature Communications ◽

10.1038/s41467-020-20306-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Mohammad M. Karimi ◽

Ya Guo ◽

Xiaokai Cui ◽

Husayn A. Pallikonda ◽

Veronika Horková ◽

...

Keyword(s):

Gene Expression ◽

T Cell ◽

Single Cell ◽

Signal Strength ◽

Gene Activity ◽

Cytotoxic T Cell ◽

Individual Selection ◽

Cell Lineages ◽

Single Cell Rna Sequencing ◽

Tcr Signals

AbstractCD4 and CD8 mark helper and cytotoxic T cell lineages, respectively, and serve as coreceptors for MHC-restricted TCR recognition. How coreceptor expression is matched with TCR specificity is central to understanding CD4/CD8 lineage choice, but visualising coreceptor gene activity in individual selection intermediates has been technically challenging. It therefore remains unclear whether the sequence of coreceptor gene expression in selection intermediates follows a stereotypic pattern, or is responsive to signaling. Here we use single cell RNA sequencing (scRNA-seq) to classify mouse thymocyte selection intermediates by coreceptor gene expression. In the unperturbed thymus, Cd4+Cd8a- selection intermediates appear before Cd4-Cd8a+ selection intermediates, but the timing of these subsets is flexible according to the strength of TCR signals. Our data show that selection intermediates discriminate MHC class prior to the loss of coreceptor expression and suggest a model where signal strength informs the timing of coreceptor gene activity and ultimately CD4/CD8 lineage choice.

Download Full-text