Single-cell Gene Expression Profiling Research Articles

Identification of Distinct Breast Cancer Stem Cell Populations Based on Single-Cell Analyses of Functionally Enriched Stem and Progenitor Pools

SummaryThe identification of breast cancer cell subpopulations featuring truly malignant stem cell qualities is a challenge due to the complexity of the disease and lack of general markers. By combining extensive single-cell gene expression profiling with three functional strategies for cancer stem cell enrichment including anchorage-independent culture, hypoxia, and analyses of low-proliferative, label-retaining cells derived from mammospheres, we identified distinct stem cell clusters in breast cancer. Estrogen receptor (ER)α+ tumors featured a clear hierarchical organization with switch-like and gradual transitions between different clusters, illustrating how breast cancer cells transfer between discrete differentiation states in a sequential manner. ERα− breast cancer showed less prominent clustering but shared a quiescent cancer stem cell pool with ERα+ cancer. The cellular organization model was supported by single-cell data from primary tumors. The findings allow us to understand the organization of breast cancers at the single-cell level, thereby permitting better identification and targeting of cancer stem cells.

A Single-Cell Gene-Expression Profile Reveals Inter-Cellular Heterogeneity within Human Monocyte Subsets.

Human monocytes are a heterogeneous cell population classified into three different subsets: Classical CD14++CD16-, intermediate CD14++CD16+, and non-classical CD14+CD16++ monocytes. These subsets are distinguished by their differential expression of CD14 and CD16, and unique gene expression profile. So far, the variation in inter-cellular gene expression within the monocyte subsets is largely unknown. In this study, the cellular variation within each human monocyte subset from a single healthy donor was described by using a novel single-cell PCR gene-expression analysis tool. We investigated 86 different genes mainly encoding cell surface markers, and proteins involved in immune regulation. Within the three human monocyte subsets, our descriptive findings show multimodal expression of key immune response genes, such as CD40, NFⱪB1, RELA, TLR4, TLR8 and TLR9. Furthermore, we discovered one subgroup of cells within the classical monocytes, which showed alterations of 22 genes e.g. IRF8, CD40, CSF1R, NFⱪB1, RELA and TNF. Additionally one subgroup within the intermediate and non-classical monocytes also displayed distinct gene signatures by altered expression of 8 and 6 genes, respectively. Hence the three monocyte subsets can be further subdivided according to activation status and differentiation, independently of the traditional classification based on cell surface markers. Demonstrating the use and the ability to discover cell heterogeneity within defined populations of human monocytes is of great importance, and can be useful in unravelling inter-cellular variation in leukocyte populations, identifying subpopulations involved in disease pathogenesis and help tailor new therapies.

SCExV: a webtool for the analysis and visualisation of single cell qRT-PCR data

BackgroundSingle cell gene expression assays have become a powerful tool with which to dissect heterogeneous populations. While methods and software exist to interrogate such data, what has been lacking is a unified solution combining analysis and visualisation which is also accessible and intuitive for use by non-bioinformaticians, as well as bioinformaticians.ResultsWe present the Single cell expression visualiser (SCExV), a webtool developed to expedite the analysis of single cell qRT-PCR data. SCExV is able to take any data matrix of Ct values as an input, but can handle files exported by the Fluidigm Biomark platform directly. In addition, SCExV also accepts and automatically integrates cell surface marker intensity values which are measured during index sorting. This allows the user to directly visualise relationships between a single cell gene expression profile and the immunophenotype of the interrogated cell.ConclusionsSCExV is a freely available webtool created to import, filter, analyse, and visualise single cell gene expression data whilst being able to simultaneously consider cellular immunophenotype. SCExV is designed to be intuitive to use whilst maintaining advanced functionality and flexibility in how analyses are performed.

A computational strategy for predicting lineage specifiers in stem cell subpopulations

Stem cell differentiation is a complex biological event. Our understanding of this process is partly hampered by the co-existence of different cell subpopulations within a given population, which are characterized by different gene expression states driven by different underlying transcriptional regulatory networks (TRNs). Such cellular heterogeneity has been recently explored with the modern single-cell gene expression profiling technologies, such as single-cell RT-PCR and RNA-seq. However, the identification of cell subpopulation-specific TRNs and genes determining specific lineage commitment (i.e., lineage specifiers) remains a challenge due to the slower development of appropriate computational and experimental workflows. Here, we propose a computational method for predicting lineage specifiers for different cell subpopulations in binary-fate differentiation events. Our method first reconstructs subpopulation-specific TRNs, which is more realistic than reconstructing a single TRN representing multiple cell subpopulations. Then, it predicts lineage specifiers based on a model that assumes that each parental stem cell subpopulation is in a stable state maintained by its specific TRN stability core. In addition, this stable state is maintained in the parental cell subpopulation by the balanced gene expression pattern of pairs of opposing lineage specifiers for mutually exclusive different daughter cell subpopulations. To this end, we devised a statistical metric for identifying opposing lineage specifier pairs that show a significant ratio change upon differentiation. Application of this computational method to three different stem cell systems predicted known and putative novel lineage specifiers, which could be experimentally tested. Our method does not require pre-selection of putative candidate genes, and can be applied to any binary-fate differentiation system for which single-cell gene expression data are available. Furthermore, this method is compatible with both single-cell RT-PCR and single-cell RNA-seq data. Given the increasing importance of single-cell gene expression data in stem cell biology and regenerative medicine, approaches like ours would be useful for the identification of lineage specifiers and their associated TRN stability cores.

Gene expression profiling of citrulline-specific B cells in rheumatoid arthritis (BA3P.118)

Abstract Rheumatoid arthritis (RA) affects ~1% of the world’s population and is the most common autoimmune disease afflicting humans. The elicitation of anti-citrulinated protein antibody (ACPA) responses, has been a major milestone for both the detection and treatment of RA. Given the significance of ACPA in RA, B cells have a central role in the progression of the disease. The identification of the differentiation state and phenotype of autoreactive B cells (ABC) is significant from both a fundamental standpoint in understanding autoimmune biology and from a clinical standpoint in providing new avenues to targeting ABCs. In order to identify novel biomarkers targeting ABCs, we have undertaken comprehensive gene expression profiling of citrulline-specific autoreactive B cells isolated from the peripheral blood of RA patients. We demonstrate the ability to selectively isolate autoreactive B cells for single-cell gene expression profiling. The frequency of citrulline-specific B cells was 0.012 % of the PBMCs. The specificity of the mAb isolated using our approach was confirmed using ELISA. We have undertaken whole transcriptome amplification and single-cell gene expression, and by comparison to pathogen-specific B cells, describe sets of genes that may be used to uniquely define ABCs. We anticipate that the key biomarkers identified from these genes will further improve our understanding of autoimmune pathology and will be evaluated for their clinical relevance.

208. Single-Cell Gene Expression Profiles of Genetically Modified T Cells for Adoptive Immunotherapy

System-level characterization of cell states requires detailed portrayal of cellular components such as gene transcripts or proteins. Here, we exploit a robust single-cell platform technology to characterize T cells expanded ex vivo intended for human application. Coupled with advanced custom analytics, we identify “drivers” (relatively few, but vital) genes which orchestrate T-cell activation and clonal expansion programming, versus “passengers” (relatively many, less critical) or resultant/downstream transcripts.Detailed analyses reveal that there is considerable variability between individual activated T cells, implying that cell state may undergo multiple stepwise transitions in a stochastic manner. Compared to interrogation of bulk T-cell populations (e.g., analyzed using RNA-Seq, microarrays, and Northern blotting), where heterogeneity in expression signals is attenuated by temporal and cell-cycle averaging, the genetic heterogeneity between cells in isogenic populations may be a reflection of fundamental phenomena such as transcriptional bursting. Our model describes cell state oscillation in tandem with transcriptional repressive (closed chromatin) and permissive conformations (open chromatin), versus simpler probabilistic models of transcription. T-cell variability can significantly impact upon individual cell behavior within seemly isogenic populations, and may be essential in molding survival and cytotoxicity within certain contexts, such as the rapidly changing and stressful tumor microenvironments. We find that lactate dehydrogenase A (LDHA) is a leading indicator for the metabolic adaptation during T-cell activation and together with other energetics related genes such pyruvate kinase isoform 2 (PKM2), can be harnessed to adjust our culture conditions during ex vivo expansion. These cellular fingerprints suggest useful ways for tuning the balance between oxidative and non-oxidative glucose metabolism to enhance the killing and thus therapeutic potential of our T cells. Our framework to harness catalogs of gene expression data into clinically-applicable insights can be adapted for meaningful characterization of other therapeutically-relevant source populations such as hematopoietic stem cells and reprogrammed NK cells. This is being reduced to practice by understanding the heterogeneity within and between genetically modified T-cell products generated for clinical use.

Two distinct effector memory cell populations of WT1 (Wilms' tumor gene 1)-specific cytotoxic T lymphocytes in acute myeloid leukemia patients.

Wilms' tumor gene 1 (WT1) protein is a promising tumor-associated antigen for cancer immunotherapy. We have been performing WT1 peptide vaccination with good clinical responses in over 750 patients with leukemia or solid cancers. In this study, we generated single-cell gene-expression profiles of the effector memory (EM) subset of WT1-specific cytotoxic T lymphocytes (CTLs) in peripheral blood of nine acute myeloid leukemia patients treated with WT1 peptide vaccine, in order to discriminate responders (WT1 mRNA levels in peripheral blood decreased to undetectable levels, decreased but stayed at abnormal levels, were stable at undetectable levels, or remained unchanged from the initial abnormal levels more than 6 months after WT1 vaccination) from non-responders (leukemic blast cells and/or WT1 mRNA levels increased relative to the initial state within 6 months of WT1 vaccination) prior to WT1 vaccination. Cluster and principal component analyses performed using 83 genes did not discriminate between responders and non-responders prior to WT1 vaccination. However, these analyses revealed that EM subset of WT1-specific CTLs could be divided into two groups: the "activated" and "quiescent" states; in responders, EM subset of the CTLs shifted to the "quiescent" state, whereas in non-responders, those shifted to the "activated" state following WT1 vaccination. These results demonstrate for the first time the existence of two distinct EM states, each of which was characteristic of responders or non-responders, of WT1-specific CTLs in AML patients, and raises the possibility of using advanced gene-expression profile analysis to clearly discriminate between responders and non-responders prior to WT1 vaccination.

Decoding the regulatory network of early blood development from single-cell gene expression measurements.

Here we report the use of diffusion maps and network synthesis from state transition graphs to better understand developmental pathways from single cell gene expression profiling. We map the progression of mesoderm towards blood in the mouse by single-cell expression analysis of 3,934 cells, capturing cells with blood-forming potential at four sequential developmental stages. By adapting the diffusion plot methodology for dimensionality reduction to single-cell data, we reconstruct the developmental journey to blood at single-cell resolution. Using transitions between individual cellular states as input, we develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model that recapitulates blood development. Model predictions were validated by showing that Sox7 inhibits primitive erythropoiesis, and that Sox and Hox factors control early expression of Erg. We therefore demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that control organogenesis.

Quantification of cell identity from single-cell gene expression profiles.

The definition of cell identity is a central problem in biology. While single-cell RNA-seq provides a wealth of information regarding cell states, better methods are needed to map their identity, especially during developmental transitions. Here, we use repositories of cell type-specific transcriptomes to quantify identities from single-cell RNA-seq profiles, accurately classifying cells from Arabidopsis root tips and human glioblastoma tumors. We apply our approach to single cells captured from regenerating roots following tip excision. Our technique exposes a previously uncharacterized transient collapse of identity distant from the injury site, demonstrating the biological relevance of a quantitative cell identity index.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0580-x) contains supplementary material, which is available to authorized users.

Variable activation of the DNA damage response pathways in patients undergoing single-photon emission computed tomography myocardial perfusion imaging.

Although single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) has improved the diagnosis and risk stratification of patients with suspected coronary artery disease, it remains a primary source of low-dose radiation exposure for cardiac patients. To determine the biological effects of low-dose radiation from SPECT MPI, we measured the activation of the DNA damage response pathways using quantitative flow cytometry and single-cell gene expression profiling. Blood samples were collected from patients before and after SPECT MPI (n=63). Overall, analysis of all recruited patients showed no marked differences in the phosphorylation of proteins (H2AX, protein 53, and ataxia telangiectasia mutated) after SPECT. The majority of patients also had either downregulated or unchanged expression in DNA damage response genes at both 24 and 48 hours post-SPECT. Interestingly, a small subset of patients with increased phosphorylation had significant upregulation of genes associated with DNA damage, whereas those with no changes in phosphorylation had significant downregulation or no difference, suggesting that some patients may potentially be more sensitive to low-dose radiation exposure. Our findings showed that SPECT MPI resulted in a variable activation of the DNA damage response pathways. Although only a small subset of patients had increased protein phosphorylation and elevated gene expression postimaging, continued care should be taken to reduce radiation exposure to both the patients and operators.

Functional and Molecular Characterization of Serial Killer CAR+ T Cells Demonstrates Adaptive Modification of Fate and Behavior Based on Tumor Cell Density

T cells genetically modified to express a CD19-specific chimeric antigen receptor (CAR) for the investigational treatment of B-cell malignancies comprise a heterogeneous population, and their ability to persist and participate in serial killing of tumor cells is a predictor of therapeutic success. We developed Timelapse Imaging Microscopy In Nanowell Grids (TIMING) to dynamically analyze thousands of interactions between T cells and tumor cells. We showed that the ability of killer CAR+ T cells to adaptively modify both cell-intrinsic (polarization and motility) and extrinsic (conjugation and kinetics of tumor-cell death) behavior was modulated by number of tumor cell encounters. Our results further demonstrate that while CAR+ T cells launch fully-competent anti-tumor responses as defined by polarized motility, serial-killing and IFN-γ secretion, the cytolytic functionality of CAR+ T cells must be quantified in the context of their ability to resist apoptosis. By comparing both the serial killer and single killer CAR+ T cells it appears that the propensity and kinetics of CAR+ T cells apoptosis was markedly different: single target killer effectors underwent apoptosis at higher frequencies (50%), than serial killers (28%) and with faster kinetics (time to apoptosis: 256±12 minutes, single target; vs. 396±25 minutes, multiple targets; p-value < 0.0001) (Figure 1). These data indicate that activation for lysis through multiple targets as opposed to prolonged contact with a single target reduces the propensity for effector apoptosis. Although the mechanistic basis for the responsiveness of these T cells to antigen/target density is not known, it is conceivable that the continuous propagation of these cells on irradiated aAPC at defined ratios, allows for balanced activation and minimized AICD. These data could provide mechanistic insights into observations that infused CAR+ T cells swell in number and produce high levels of cytokine in response to addressing large numbers of CD19+ tumor cells, but then decline in number as the tumor burden is lowered due to the serial killing by effector T cells. Our findings emphasize the notion that motility and persistence (resistance to apoptosis), in addition to direct cytolytic activity, may serve as criteria to rapidly evaluate CAR+ T-cell potency.In order to further define functional CAR+ T cells at the single-cell level, we identified individual killer CAR+ T cells based on their interaction with CD19-expressing EL4 tumor cells. These killer CAR+ T cells were retrieved and subjected to single-cell gene expression profiling. The expression profiles were then fitted using a logistic regression model with an implicit gene selector function to identify sets of candidate genes that uniquely distinguish (non-apoptotic) killers and non-killers. We anticipate that our findings and the TIMING methodology may provide a rapid approach for assessing the impact of CAR designs and sub-populations of cells modified to redirect specificity. [Display omitted] [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Characterizing heterogeneity in leukemic cells using single-cell gene expression analysis.

BackgroundA fundamental challenge for cancer therapy is that each tumor contains a highly heterogeneous cell population whose structure and mechanistic underpinnings remain incompletely understood. Recent advances in single-cell gene expression profiling have created new possibilities to characterize this heterogeneity and to dissect the potential intra-cancer cellular hierarchy.ResultsHere, we apply single-cell analysis to systematically characterize the heterogeneity within leukemic cells using the MLL-AF9 driven mouse model of acute myeloid leukemia. We start with fluorescence-activated cell sorting analysis with seven surface markers, and extend by using a multiplexing quantitative polymerase chain reaction approach to assay the transcriptional profile of a panel of 175 carefully selected genes in leukemic cells at the single-cell level. By employing a set of computational tools we find striking heterogeneity within leukemic cells. Mapping to the normal hematopoietic cellular hierarchy identifies two distinct subtypes of leukemic cells; one similar to granulocyte/monocyte progenitors and the other to macrophage and dendritic cells. Further functional experiments suggest that these subtypes differ in proliferation rates and clonal phenotypes. Finally, co-expression network analysis reveals similarities as well as organizational differences between leukemia and normal granulocyte/monocyte progenitor networks.ConclusionsOverall, our single-cell analysis pinpoints previously uncharacterized heterogeneity within leukemic cells and provides new insights into the molecular signatures of acute myeloid leukemia.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0525-9) contains supplementary material, which is available to authorized users.

Identifying functional gene regulatory network phenotypes underlying single cell transcriptional variability

Recent analysis of single-cell transcriptomic data has revealed a surprising organization of the transcriptional variability pervasive across individual neurons. In response to distinct combinations of synaptic input-type, a new organization of neuronal subtypes emerged based on transcriptional states that were aligned along a gradient of correlated gene expression. Individual neurons traverse across these transcriptional states in response to cellular inputs. However, the regulatory network interactions driving these changes remain unclear. Here we present a novel fuzzy logic-based approach to infer quantitative gene regulatory network models from highly variable single-cell gene expression data. Our approach involves developing an a priori regulatory network that is then trained against in vivo single-cell gene expression data in order to identify causal gene interactions and corresponding quantitative model parameters. Simulations of the inferred gene regulatory network response to experimentally observed stimuli levels mirrored the pattern and quantitative range of gene expression across individual neurons remarkably well. In addition, the network identification results revealed that distinct regulatory interactions, coupled with differences in the regulatory network stimuli, drive the variable gene expression patterns observed across the neuronal subtypes. We also identified a key difference between the neuronal subtype-specific networks with respect to negative feedback regulation, with the catecholaminergic subtype network lacking such interactions. Furthermore, by varying regulatory network stimuli over a wide range, we identified several cases in which divergent neuronal subtypes could be driven towards similar transcriptional states by distinct stimuli operating on subtype-specific regulatory networks. Based on these results, we conclude that heterogenous single-cell gene expression profiles should be interpreted through a regulatory network modeling perspective in order to separate the contributions of network interactions from those of cellular inputs.

Abstract LB-49: Defining hierarchical organization of breast cancer cells and cancer activated fibroblasts using single-cell gene expression profiling: switch-like and transient transitions between novel stem cell and progenitor pools

Abstract The cancer stem cell (CSC) model holds that cancers are organized in a hierarchical structure and derived from cellular populations with stem cell properties. CSC characteristics include self-renewal, tumorigenicity, multi-lineage differentiation as well as increased resistance to radio- and chemotherapy-induced cell death. In order to better understand the complex hierarchical structure of breast cancer cells in vivo as well as phenotypes of stromal cells as cancer associated fibroblasts, tools need to be developed, enabling analyses of key regulators and biomarkers on a single-cell level. We therefore established single-cell reverse transcription quantitative real-time PCR and gene expression profiling, including more than 100 key regulators associated with the cell cycle, differentiation, EMT, stemness and fibroblast activation and performed extensive single-cell PCR analyses, using cell lines and primary breast cancer samples. Three strategies were used to enrich for cancer stem cells and progenitor populations corresponding to 1) cells surviving anchorage-independent growth conditions, 2) cells in mammospheres showing few cell divisions, defined by a cell division tracker and 3) cells grown under hypoxic conditions. All methods have earlier been shown to enrich for cells that have an increased cancer initiating ability in mouse model systems. Interestingly, all strategies increased two separate tumor subpopulations defined by lower proliferative features and various EPCAM, ERBB2, CDH1, ID1, ABCG2, POU5F1, NANOG and SOX2 levels. Besides, a clear switch-like transition between the various populations, a transition state linking the cancer stem cell and progenitor populations with the high proliferative main cluster was observed. When specifically analyzing the few cells that had only undergone one asymmetrical cell division in 5 days mammosphere cultures, it was clear that this potential stem cell pool had a distinctive expression profile based on the included key regulators. We next analyzed immortalized primary fibroblasts and tumor activated counterparts using single-cell PCR analyses. The two cell types clearly separated from each other and showed various activation states defined by key regulators as COL1A1, ALDH1A3, TGFB, PDGFA, ACTA2, CXCL12 and cell cycle regulators. The data support a continuing transition towards myofibroblast differentiation with gradual loss of proliferation, whereas the activation markers peak in an intermediate state before entering a final low proliferative condition. When analyzing primary tumors we observed distinct clusters of epithelial cells and fibroblasts supporting the validity of the defined assay. A detailed description of primary breast cancer samples and important microenvironmental components combined with functional assays on a single cell level are now ongoing. Within this comprehensive program we have identified diverse previously unknown CSC/progenitor pools or distinct cellular states and we are now able to model how cells actually transfer between various differentiation and stem cells states, thereby better understanding the hierarchical composition of breast cancer on a single cell level. Citation Format: Anders Ståhlberg, Nina Akrap, Daniel Andersson, Pernilla Gregersson, Susann Busch, Göran P. Landberg. Defining hierarchical organization of breast cancer cells and cancer activated fibroblasts using single-cell gene expression profiling: switch-like and transient transitions between novel stem cell and progenitor pools. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-49. doi:10.1158/1538-7445.AM2014-LB-49

Multiplexed in situ hybridization using hybridization chain reaction.

ZebrafishVol. 11, No. 5 TechnoFishMultiplexed In Situ Hybridization Using Hybridization Chain ReactionHarry M.T. Choi, Victor A. Beck, and Niles A. PierceHarry M.T. ChoiDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, California.Search for more papers by this author, Victor A. BeckDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, California.Search for more papers by this author, and Niles A. PierceDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, California.Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California.Search for more papers by this authorPublished Online:19 Sep 2014https://doi.org/10.1089/zeb.2014.1501AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"Multiplexed In Situ Hybridization Using Hybridization Chain Reaction." Zebrafish, 11(5), pp. 488–489FiguresReferencesRelatedDetailsCited byNanoDam identifies Homeobrain (ARX) and Scarecrow (NKX2.1) as conserved temporal factors in the Drosophila central brain and visual systemDevelopmental Cell, Vol. 57, No. 9Enhanced detection of expanded repeat mRNA foci with hybridization chain reaction23 April 2021 | Acta Neuropathologica Communications, Vol. 9, No. 1Channel nuclear pore complex subunits are required for transposon silencing in Drosophila15 April 2021 | eLife, Vol. 10RNA folding kinetics using Monte Carlo and Gillespie algorithms5 August 2017 | Journal of Mathematical Biology, Vol. 76, No. 5The Secret Life of RNA: Lessons from Emerging Methodologies13 November 2017Resolving in vivo gene expression during collective cell migration using an integrated RNAscope, immunohistochemistry and tissue clearing methodMechanisms of Development, Vol. 148Multidisciplinary approaches to understanding collective cell migration in developmental biology1 June 2016 | Open Biology, Vol. 6, No. 6Quantitative single cell gene expression profiling in the avian embryo27 May 2015 | Developmental Dynamics, Vol. 244, No. 6Structural diversity measures for RNA Volume 11Issue 5Oct 2014 InformationCopyright 2014, Mary Ann Liebert, Inc.To cite this article:Harry M.T. Choi, Victor A. Beck, and Niles A. Pierce.Multiplexed In Situ Hybridization Using Hybridization Chain Reaction.Zebrafish.Oct 2014.488-489.http://doi.org/10.1089/zeb.2014.1501Published in Volume: 11 Issue 5: September 19, 2014Online Ahead of Print:September 4, 2014PDF download

Removal of reprogramming transgenes improves the tissue reconstitution potential of keratinocytes generated from human induced pluripotent stem cells.

Human induced pluripotent stem cell (hiPSC) lines have a great potential for therapeutics because customized cells and organs can be induced from such cells. Assessment of the residual reprogramming factors after the generation of hiPSC lines is required, but an ideal system has been lacking. Here, we generated hiPSC lines from normal human dermal fibroblasts with piggyBac transposon bearing reprogramming transgenes followed by removal of the transposon by the transposase. Under this condition, we compared the phenotypes of transgene-residual and -free hiPSCs of the same genetic background. The transgene-residual hiPSCs, in which the transcription levels of the reprogramming transgenes were eventually suppressed, were quite similar to the transgene-free hiPSCs in a pluripotent state. However, after differentiation into keratinocytes, clear differences were observed. Morphological, functional, and molecular analyses including single-cell gene expression profiling revealed that keratinocytes from transgene-free hiPSC lines were more similar to normal human keratinocytes than those from transgene-residual hiPSC lines, which may be partly explained by reactivation of residual transgenes upon induction of keratinocyte differentiation. These results suggest that transgene-free hiPSC lines should be chosen for therapeutic purposes.

Integration of Public Microarray Data with Single Cell Gene Expression Profiling Facilitates Identification of Transcriptionally-Defined Subpopulations in Small Cell Lung Cancer

Over the last fifteen years DNA microarray analysis has yielded tremendous insight into the nature of cancer cell populations, leading to numerous advances in treatment strategies. However, it has become increasingly clear that these tumors are highly heterogeneous, with subsets of cancer stem cells believed to be responsible for the tumorigenic capacity of each parent population. Single cell gene expression analysis represents an attractive approach to investigate such complex cell populations for which the granularity afforded by traditional microarray-based analyses is generally insufficient.

Measuring T cell receptor and T cell gene expression diversity in antigen-responsive human CD4+ T cells

T cells have diversity in TCR, epitope recognition, and cytokine production, and can be used for immune monitoring. Furthermore, clonal expansion of TCR families in disease may provide opportunities for TCR-directed therapies. We developed methodology for sequencing expressed genes of TCR alpha and beta chains from single cells and applied this to vaccine (tetanus-toxoid)-responsive CD4+ T cells. TCR alpha and beta chains were both successfully sequenced in 1309 (43%) of 3038 CD4+ T cells yielding 677 different receptors. TRAV and TRBV gene usage differed between tetanus-toxoid-responsive and non-responsive cells (p=0.004 and 0.0002), and there was extensive TCR diversity in tetanus-toxoid-responsive cells within individuals. Identical TCRs could be recovered in different samples from the same subject: TCRs identified after booster vaccination were frequent in pre-booster memory T cells (31% of pre-booster TCR), and also identified in pre-booster vaccination naïve cells (6.5%). No TCR was shared between subjects, but tetanus toxoid-responsive cells sharing one of their TCR chains were observed within and between subjects. Coupling single-cell gene expression profiling to TCR sequencing revealed examples of distinct cytokine profiles in cells bearing identical TCR. Novel molecular methodology demonstrates extensive diversity of Ag-responsive CD4+ T cells within and between individuals.

Heterogeneity of Astrocytes: From Development to Injury – Single Cell Gene Expression

Astrocytes perform control and regulatory functions in the central nervous system; heterogeneity among them is still a matter of debate due to limited knowledge of their gene expression profiles and functional diversity. To unravel astrocyte heterogeneity during postnatal development and after focal cerebral ischemia, we employed single-cell gene expression profiling in acutely isolated cortical GFAP/EGFP-positive cells. Using a microfluidic qPCR platform, we profiled 47 genes encoding glial markers and ion channels/transporters/receptors participating in maintaining K+ and glutamate homeostasis per cell. Self-organizing maps and principal component analyses revealed three subpopulations within 10–50 days of postnatal development (P10–P50). The first subpopulation, mainly immature glia from P10, was characterized by high transcriptional activity of all studied genes, including polydendrocytic markers. The second subpopulation (mostly from P20) was characterized by low gene transcript levels, while the third subpopulation encompassed mature astrocytes (mainly from P30, P50). Within 14 days after ischemia (D3, D7, D14), additional astrocytic subpopulations were identified: resting glia (mostly from P50 and D3), transcriptionally active early reactive glia (mainly from D7) and permanent reactive glia (solely from D14). Following focal cerebral ischemia, reactive astrocytes underwent pronounced changes in the expression of aquaporins, nonspecific cationic and potassium channels, glutamate receptors and reactive astrocyte markers.

Specification of CD8+ T lymphocyte fates during adaptive immunity revealed by single cell gene expression analyses (P1448)

Abstract Although it is well established that heterogeneity of lymphocyte fate is an essential feature of adaptive immune responses, how and when these divergent cellular fates are specified remains unknown. It has been previously shown that a T lymphocyte responding to a microbial infection can undergo asymmetric division to yield two daughter cells that are differentially fated from inception. Such a model suggests that the progeny arising from each of the two differentially fated daughter cells might exhibit distinct patterns of gene expression. Because strategies analyzing bulk cell populations cannot distinguish differences among individual cells, we applied high-throughput single cell gene expression profiling to interrogate single CD8+ T lymphocytes throughout the course of an immune response to a bacterial infection. We demonstrate that terminally differentiated effector cells and self-renewing memory cells exhibit unique gene expression profiles. Moreover, we are able to identify cells early during the immune response, beginning with the first cell division, that exhibit distinct gene expression patterns characteristic of effector or memory cells. Finally, we derive a network model that allows us to predict the sequential activation of genes that orchestrate each cellular fate, providing a comprehensive view of CD8+ T lymphocyte differentiation. Together, these results provide novel insights into the specification of lymphocyte fates during an immune response.