Cell-type Signatures Research Articles

Characterization of CD4+ T cells expressing an elevated interferon-stimulated gene signature in SLE patients

Abstract The type I interferon (IFN) signaling pathway plays a key role in Systemic Lupus Erythematosus (SLE) pathology, and a majority of patients display elevated expression levels of interferon-stimulated genes (ISGs) in the peripheral blood. However, it is unclear which immune cells are responding to IFN to drive disease pathology. We compared the ISG signatures of cell types sorted from peripheral blood of IFNβ-treated relapsing-remitting multiple sclerosis (RRMS) patients with those of SLE patients. While systemic IFNβ treatment stimulated ISG expression in the monocytes and neutrophils of RRMS patients, the ISG signature in SLE patients was primarily detected in CD4+ T and B cells. This suggests these cells may be exposed to higher levels of IFN in microenvironments of inflammation within SLE patients, such as upon interaction with APC presenting SLE antigens. We hypothesize that a subset of primarily antigen-specific CD4+ T cells expand and express ISGs during SLE disease activity, and that these cells and their signature pathways can serve as disease biomarkers and therapeutic targets. To examine ISG expression by CD4+ T cells in SLE, we compared the expression of several ISG-correlated surface markers between high- and low-IFN score patients and healthy controls. Subsets of CD4+ T cells in high-IFN score patients expressed elevated levels of PD1, CD66a, and OX40, while the majority of cells expressed a similar level as cells in healthy controls. This suggests that only a small subset of CD4+ T cells is activated by localized IFN sources and is involved in SLE pathology. We have further characterized PD1+, OX40+ CD66a+, and SLE-specific memory CD4+ T cells by RNAseq and flow cytometry.

Integration of microRNA signatures of distinct mammary epithelial cell types with their gene expression and epigenetic portraits.

IntroductionMicroRNAs (miRNAs) have been implicated in governing lineage specification and differentiation in multiple organs; however, little is known about their specific roles in mammopoiesis. We have determined the global miRNA expression profiles of functionally distinct epithelial subpopulations in mouse and human mammary tissue, and compared these to their cognate transcriptomes and epigenomes. Finally, the human miRNA signatures were used to interrogate the different subtypes of breast cancer, with a view to determining miRNA networks deregulated during oncogenesis.MethodsRNA from sorted mouse and human mammary cell subpopulations was subjected to miRNA expression analysis using the TaqMan MicroRNA Array. Differentially expressed (DE) miRNAs were correlated with gene expression and histone methylation profiles. Analysis of miRNA signatures of the intrinsic subtypes of breast cancer in The Cancer Genome Atlas (TCGA) database versus those of normal human epithelial subpopulations was performed.ResultsUnique miRNA signatures characterized each subset (mammary stem cell (MaSC)/basal, luminal progenitor, mature luminal, stromal), with a high degree of conservation across species. Comparison of miRNA and transcriptome profiles for the epithelial subtypes revealed an inverse relationship and pinpointed key developmental genes. Interestingly, expression of the primate-specific miRNA cluster (19q13.4) was found to be restricted to the MaSC/basal subset. Comparative analysis of miRNA signatures with H3 lysine modification maps of the different epithelial subsets revealed a tight correlation between active or repressive marks for the top DE miRNAs, including derepression of miRNAs in Ezh2-deficient cellular subsets. Interrogation of TCGA-identified miRNA profiles with the miRNA signatures of different human subsets revealed specific relationships.ConclusionsThe derivation of global miRNA expression profiles for the different mammary subpopulations provides a comprehensive resource for understanding the interplay between miRNA networks and target gene expression. These data have highlighted lineage-specific miRNAs and potential miRNA–mRNA networks, some of which are disrupted in neoplasia. Furthermore, our findings suggest that key developmental miRNAs are regulated by global changes in histone modification, thus linking the mammary epigenome with genome-wide changes in the expression of genes and miRNAs. Comparative miRNA signature analyses between normal breast epithelial cells and breast tumors confirmed an important linkage between luminal progenitor cells and basal-like tumors.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-015-0585-0) contains supplementary material, which is available to authorized users.

RNA-Seq analysis of enteroendocrine cells reveals a role for FABP5 in the control of GIP secretion.

In response to fat intake, enteroendocrine K cells release the hormone glucose-dependent insulinotropic polypeptide (GIP). GIP acts on adipocytes to increase lipid uptake and enhance adipokine secretion, promoting weight gain and insulin resistance. Modulation of intestinal GIP release could therefore represent a therapeutic strategy for the treatment and prevention of obesity and diabetes. However, the prospects of using drugs to effectively target specific enteroendocrine cell types have been tempered by the realization that these cells share similar transcriptional programs and frequently employ common mechanisms of hormone secretion. To gain novel insights into the regulation of GIP release, we generated knock-in mice expressing green fluorescent protein (GFP) under the control of the endogenous GIP promoter that enable the isolation of a purified population of small intestine K cells. Using RNA sequencing, we comprehensively characterized the transcriptomes of GIP(GFP) cells as well as the entire enteroendocrine lineage derived from Neurogenin3-expressing progenitors. Among the genes differentially expressed in GIP(GFP) cells, we identified and validated fatty acid-binding protein 5 (FABP5) as a highly expressed marker of GIP-producing cells that is absent in other enteroendocrine cell types. FABP5 promotes intracellular transport and inactivation of endocannabinoids, including anandamide, which inhibits GIP release. Remarkably, we found that circulating levels of GIP were significantly decreased in FABP5-deficient mice in the fasting state and in response to acute, oral fat diet administration. Our findings highlight the power of RNA sequencing to uncover molecular signatures of specific enteroendocrine cell types that can potentially be exploited for therapeutic purposes in the treatment of metabolic disorders.

Micro RNA detection in long-term fixed tissue of cortical glutamatergic pyramidal neurons after targeted laser-capture neuroanatomical microdissection.

Formalin fixation (FF) is the standard and most common method for preserving postmortem brain tissue. FF stabilizes cellular morphology and tissue architecture, and can be used to study the distinct morphologic and genetic signatures of different cell types. Although the procedure involved in FF degrades messenger RNA over time, an alternative approach is to use small RNAs (sRNAs) for genetic analysis associated with cell morphology. Although genetic analysis is carried out on fresh or frozen tissue, there is limited availability or impossibility on targeting specific cell populations, respectively. The goal of this study is to detect miRNA and other classes of sRNA stored in formalin or in paraffin embedded for over decades. Two brain samples, one formed by a mixed population of cortical and subcortical cells, and one formed by pyramidal shaped cells collected by laser-capture microdissection, were subjected to sRNA sequencing. Performing bioinformatics analysis over the sequenced sRNA from brain tissue, we detected several classes of sRNA, such as miRNAs that play key roles in brain neurodevelopmental and maintenance pathways, and hsa-mir-155 expression in neurons. Comparison with existing method: Our method is the first to combine the approaches for: laser-capture of pyramidal neurons from long-term formalin-fixed brain; extract sRNA from laser-captured pyramidal neurons; apply a suite of bioinformatics tools to detect miRNA and other classes of sRNAs on sequenced samples having high levels of RNA degradation. This is the first study to show that sRNA can be rescued from laser-captured FF pyramidal neurons.

A Self-Directed Method for Cell-Type Identification and Separation of Gene Expression Microarrays

Gene expression analysis is generally performed on heterogeneous tissue samples consisting of multiple cell types. Current methods developed to separate heterogeneous gene expression rely on prior knowledge of the cell-type composition and/or signatures - these are not available in most public datasets. We present a novel method to identify the cell-type composition, signatures and proportions per sample without need for a-priori information. The method was successfully tested on controlled and semi-controlled datasets and performed as accurately as current methods that do require additional information. As such, this method enables the analysis of cell-type specific gene expression using existing large pools of publically available microarray datasets.

Coexpression analysis of large cancer datasets provides insight into the cellular phenotypes of the tumour microenvironment

BackgroundBiopsies taken from individual tumours exhibit extensive differences in their cellular composition due to the inherent heterogeneity of cancers and vagaries of sample collection. As a result genes expressed in specific cell types, or associated with certain biological processes are detected at widely variable levels across samples in transcriptomic analyses. This heterogeneity also means that the level of expression of genes expressed specifically in a given cell type or process, will vary in line with the number of those cells within samples or activity of the pathway, and will therefore be correlated in their expression.ResultsUsing a novel 3D network-based approach we have analysed six large human cancer microarray datasets derived from more than 1,000 individuals. Based upon this analysis, and without needing to isolate the individual cells, we have defined a broad spectrum of cell-type and pathway-specific gene signatures present in cancer expression data which were also found to be largely conserved in a number of independent datasets.ConclusionsThe conserved signature of the tumour-associated macrophage is shown to be largely-independent of tumour cell type. All stromal cell signatures have some degree of correlation with each other, since they must all be inversely correlated with the tumour component. However, viewed in the context of established tumours, the interactions between stromal components appear to be multifactorial given the level of one component e.g. vasculature, does not correlate tightly with another, such as the macrophage.

Cell-type, allelic, and genetic signatures in the human pancreatic beta cell transcriptome

Elucidating the pathophysiology and molecular attributes of common disorders as well as developing targeted and effective treatments hinges on the study of the relevant cell type and tissues. Pancreatic beta cells within the islets of Langerhans are centrally involved in the pathogenesis of both type 1 and type 2 diabetes. Describing the differentiated state of the human beta cell has been hampered so far by technical (low resolution microarrays) and biological limitations (whole islet preparations rather than isolated beta cells). We circumvent these by deep RNA sequencing of purified beta cells from 11 individuals, presenting here the first characterization of the human beta cell transcriptome. We perform the first comparison of gene expression profiles between beta cells, whole islets, and beta cell depleted islet preparations, revealing thus beta-cell–specific expression and splicing signatures. Further, we demonstrate that genes with consistent increased expression in beta cells have neuronal-like properties, a signal previously hypothesized. Finally, we find evidence for extensive allelic imbalance in expression and uncover genetic regulatory variants (eQTLs) active in beta cells. This first molecular blueprint of the human beta cell offers biological insight into its differentiated function, including expression of key genes associated with both major types of diabetes.

An evolutionarily conserved mutual interdependence between Aire and microRNAs in promiscuous gene expression

The establishment and maintenance of central tolerance depends to a large extent on the ability of medullary thymic epithelial cells to express a variety of tissue-restricted antigens, the so-called promiscuous gene expression (pGE). Autoimmune regulator (Aire) is to date the best characterised transcriptional regulator known to at least partially coordinate pGE. There is accruing evidence that the expression of Aire-dependent and -independent genes is modulated by higher order chromatin configuration, epigenetic modifications and post-transcriptional control. Given the involvement of microRNAs (miRNAs) as potent post-transcriptional modulators of gene expression, we investigated their role in the regulation of pGE in purified mouse and human thymic epithelial cells (TECs). Microarray profiling of TEC subpopulations revealed evolutionarily conserved cell type and differentiation-specific miRNA signatures with a subset of miRNAs being significantly upregulated during terminal medullary thymic epithelial cell differentiation. The differential regulation of this subset of miRNAs was correlated with Aire expression and some of these miRNAs were misexpressed in the Aire knockout thymus. In turn, the specific absence of miRNAs in TECs resulted in a progressive reduction of Aire expression and pGE, affecting both Aire-dependent and -independent genes. In contrast, the absence of miR-29a only affected the Aire-dependent gene pool. These findings reveal a mutual interdependence of miRNA and Aire.

Microarray-Based Capture of Novel Expressed Cell Type–Specific Transfrags (CoNECT) to Annotate Tissue-Specific Transcription inDrosophila melanogaster

Faithful annotation of tissue-specific transcript isoforms is important not only to understand how genes are organized and regulated but also to identify potential novel, unannotated exons of genes, which may be additional targets of mutation in disease states or while performing mutagenic screens. We have developed a microarray enrichment methodology followed by long-read, next-generation sequencing for identification of unannotated transcript isoforms expressed in two Drosophila tissues, the ovary and the testis. Even with limited sequencing, these studies have identified a large number of novel transcription units, including 5′ exons and extensions, 3′ exons and extensions, internal exons and exon extensions, gene fusions, and both germline-specific splicing events and promoters. Additionally, comparing our capture dataset with tiling array and traditional RNA-seq analysis, we demonstrate that our enrichment strategy is able to capture low-abundance transcripts that cannot readily be identified by the other strategies. Finally, we show that our methodology can help identify transcriptional signatures of minority cell types within the ovary that would otherwise be difficult to reveal without the CoNECT enrichment strategy. These studies introduce an efficient methodology for cataloging tissue-specific transcriptomes in which specific classes of genes or transcripts can be targeted for capture and sequence, thus reducing the significant sequencing depth normally required for accurate annotation.

Integrated genome and transcriptome sequencing identifies a novel form of hybrid and aggressive prostate cancer

Next-generation sequencing is making sequence-based molecular pathology and personalized oncology viable. We selected an individual initially diagnosed with conventional but aggressive prostate adenocarcinoma and sequenced the genome and transcriptome from primary and metastatic tissues collected prior to hormone therapy. The histology-pathology and copy number profiles were remarkably homogeneous, yet it was possible to propose the quadrant of the prostate tumour that likely seeded the metastatic diaspora. Despite a homogeneous cell type, our transcriptome analysis revealed signatures of both luminal and neuroendocrine cell types. Remarkably, the repertoire of expressed but apparently private gene fusions, including C15orf21:MYC, recapitulated this biology. We hypothesize that the amplification and over-expression of the stem cell gene MSI2 may have contributed to the stable hybrid cellular identity. This hybrid luminal-neuroendocrine tumour appears to represent a novel and highly aggressive case of prostate cancer with unique biological features and, conceivably, a propensity for rapid progression to castrate-resistance. Overall, this work highlights the importance of integrated analyses of genome, exome and transcriptome sequences for basic tumour biology, sequence-based molecular pathology and personalized oncology.

Practical implications of gene-expression-based assays for breast oncologists.

Gene-expression profiling has had a considerable impact on our understanding of breast cancer biology, and more recently on clinical care. Two statistical approaches underlie these advancements. Supervised analyses have led to the development of gene-expression signatures designed to predict survival and/or treatment response, which has resulted in the development of new clinical assays. Unsupervised analyses have identified numerous biological signatures including signatures of cell type of origin, signaling pathways, and of cellular proliferation. Included within these biological signatures are the molecular subtypes known as the 'intrinsic' subtypes of breast cancer. This classification has expanded our appreciation of the heterogeneity of breast cancer and has provided a way to sub-classify the disease in a manner that might have clinical utility. In this Review, we discuss the clinical utility of gene-expression-based assays and their technical potential as clinical tools vis-a-vis the performance of breast cancer biomarkers that are the current standard of care.

Relationships of ESR1 and XBP1 expression in human breast carcinoma and stromal cells isolated by laser capture microdissection compared to intact breast cancer tissue

Results from investigations of human genomics which utilize intact tissue biopsy specimens maybe compromised due to a host of uncontrolled variables including cellular heterogeneity of a sample collected under diverse conditions, then processed and stored using different protocols. To determine the cellular origin and assess relationships of mRNA expression of two genes reported to be co-expressed in human breast carcinoma (estrogen receptor-α, ESR1 and X-box binding protein 1, XBP1), gene expression analyses were performed with intact tissue sections and compared with those of laser capture microdissection (LCM)-procured carcinoma and stromal cells from serial sections of the same tissue. Frozen sections of human breast carcinomas were first evaluated for structural integrity and pathology after hematoxylin and eosin (H&E) staining. Total RNA preparations from intact tissue sections and LCM-procured carcinoma and stromal cells were reverse transcribed for measurements of ESR1 and XBP1 expression by quantitative PCR (qPCR). These results were compared with those obtained from microarray analyses of LCM-procured carcinoma cells. Levels of ESR1 and XBP1 were detected in the intact breast cancer tissue sections suggesting coordinate gene expression. Although coordinate expression of these genes was observed in the LCM-procured carcinoma cells, it was not discerned in LCM-procured stromal cells. The origin of coordinate expression of ESR1 and XBP1 observed in whole tissue sections of human breast cancer biopsies is due principally to their co-expression in carcinoma cells and not in the surrounding stromal cells as substantiated using LCM-procured cells. Collectively, a microgenomic process was established from human tissue preparation to RNA characterization and analysis to identify molecular signatures of specific cell types predicting clinical behavior.

A multi-gene transcriptional profiling approach to the discovery of cell signature markers

A profile of transcript abundances from multiple genes constitutes a molecular signature if the expression pattern is unique to one cell type. Here we measure mRNA copy numbers per cell by normalizing per million copies of 18S rRNA and identify 6 genes (TIE1, KDR, CDH5, TIE2, EFNA1 and MYO5C) out of 79 genes tested as excellent molecular signature markers for endothelial cells (ECs) in vitro. The selected genes are uniformly expressed in ECs of 4 different origins but weakly or not expressed in 4 non-EC cell lines. A multi-gene transcriptional profile of these 6 genes clearly distinguishes ECs from non-ECs in vitro. We conclude that (i) a profile of mRNA copy numbers per cell from a well-chosen multi-gene panel can act as a sensitive and accurate cell type signature marker, and (ii) the method described here can be applied to in vivo cell fingerprinting and molecular diagnosis.

A Transcriptional Roadmap to the Induction of Pluripotency in Somatic Cells

Human embryonic stem (ES) cells possess an enormous potential for applications in regenerative medicine. However, these cells have several inevitable hurdles limiting their clinical applications, such as transplant rejection and embryo destruction. A milestone recently achieved was the derivation of induced pluripotent stem (iPS) cells by over-expressing combinations of defined transcription factors, namely, OCT4, SOX2, NANOG, and LIN28 or OCT4, SOX2, KLF4, and c-MYC. Human iPS cells exhibit many characteristics identical to those of inner cell mass-derived ES cells. Here, we summarize the generation of human fibroblast-derived iPS cells and discuss the promises and limitations of their use. In addition, by utilising numerous published transcriptome datasets related to ES cells, fibroblast-derived iPS cells, partially induced pluripotent stem cells (PiPSC) and wild type fibroblasts, we reveal similarities (self-renewal signature) and differences (donor cell-type and PiPSC signatures) in genes and associated signaling pathways operative in the induction of pluripotency in fibroblasts. In particular, we highlight that induction of ground state pluripotency is also favoured by the inhibition of epithelial mesenchymal transition (EMT) and hence the induction of mesenchymal epithelial transition (MET). We anticipate that these findings might aid in the establishment of more efficient protocols for inducing pluripotency in somatic cells.

Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1

The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM). We describe a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes and integrate multidimensional genomic data to establish patterns of somatic mutations and DNA copy number. Aberrations and gene expression of EGFR, NF1, and PDGFRA/IDH1 each define the Classical, Mesenchymal, and Proneural subtypes, respectively. Gene signatures of normal brain cell types show a strong relationship between subtypes and different neural lineages. Additionally, response to aggressive therapy differs by subtype, with the greatest benefit in the Classical subtype and no benefit in the Proneural subtype. We provide a framework that unifies transcriptomic and genomic dimensions for GBM molecular stratification with important implications for future studies.

Computational Dissection of Tumor Expression Profiles.

Abstract Gene expression profiling has yielded important insights about tumor biology that may improve treatment decisions in patients. However, it is difficult to collect a specimen of pure tumor cells, and thus microarray measurements usually reflect the contribution of tumor cells as well as stromal and other normal cells. We applied unsupervised matrix factorization methods to gene expression data to derive several sets of co-expressed genes, or signatures, that together comprise a set of independent descriptors of breast tumors. Some of these signatures correspond to specific cell types (adipocytes, lymphocytes, fibroblasts), while others reflect well-known tumor-intrinsic expression programs (ER, ERBB2, proliferation). We confirmed the specificity of the signatures using expression data from purified normal cells and tumor cell lines, microdissected tumors, and bulk tumors with corresponding histological cellularity estimates. In several data sets, cell-type signatures were highly variable and anticorrelated with tumor-intrinsic signatures, confirming that variation in normal cell content is a potential source of measurement bias.Overall, these results provide an intuitive framework for the interpetation of tumor expression profiles and may lead to an improved understanding of the physiological mechanisms involved in tumor development. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 1166.

Gene expression relationship between prostate cancer cells of Gleason 3, 4 and normal epithelial cells as revealed by cell type-specific transcriptomes

BackgroundProstate cancer cells in primary tumors have been typed CD10-/CD13-/CD24hi/CD26+/CD38lo/CD44-/CD104-. This CD phenotype suggests a lineage relationship between cancer cells and luminal cells. The Gleason grade of tumors is a descriptive of tumor glandular differentiation. Higher Gleason scores are associated with treatment failure.MethodsCD26+ cancer cells were isolated from Gleason 3+3 (G3) and Gleason 4+4 (G4) tumors by cell sorting, and their gene expression or transcriptome was determined by Affymetrix DNA array analysis. Dataset analysis was used to determine gene expression similarities and differences between G3 and G4 as well as to prostate cancer cell lines and histologically normal prostate luminal cells.ResultsThe G3 and G4 transcriptomes were compared to those of prostatic cell types of non-cancer, which included luminal, basal, stromal fibromuscular, and endothelial. A principal components analysis of the various transcriptome datasets indicated a closer relationship between luminal and G3 than luminal and G4. Dataset comparison also showed that the cancer transcriptomes differed substantially from those of prostate cancer cell lines.ConclusionsGenes differentially expressed in cancer are potential biomarkers for cancer detection, and those differentially expressed between G3 and G4 are potential biomarkers for disease stratification given that G4 cancer is associated with poor outcomes. Differentially expressed genes likely contribute to the prostate cancer phenotype and constitute the signatures of these particular cancer cell types.

Building prognostic and predictive models for cancer patients using expression modules and clinical variables.

Abstract Abstract #5063 Background: Prognostication for breast cancer patients is improving, in part through the use of genomic predictors. However, most predictors only utilize one data type that is gene expression data, and thus prognostication that simultaneously uses multiple data types may result in even more accurate predictors.
 Methods: We recently developed a unique risk of relapse predictor based upon a Cox proportional hazards model that contained gene expression (5 intrinsic subtypes) and clinical variables (tumor size and node status)(Parker ASCO 2008 abstract #11008). This model was accurate in predicting 7 year relapse probabilities and out performed the genomic or clinical variables only models. Building upon this combined genomic plus clinical parameters approach, we reasoned that including other expression modules indicative of pathway activation and cell type signatures might further improve our models. Therefore we build a collection of >200 expression modules using 1) unsupervised hierarchical and bi-clustering analysis 600 human tumors, 2) similar cluster analyses of 250 mouse mammary tumors, 3) >50 published breast tumor genomic profiles, and 4) signaling pathway activation modules derived from our own work. Each signature, or module, was then used with LASSO variable selection to determine if prognostic Cox Proportional Hazards models could be built using a local therapy and tamoxifen only treated patient set.
 Results: These analyses show that highly significant prognostic models could be built for 1) all patients, 2) ER-positive patients, and 3) Luminal patients. Less predictive, yet significant models were created Basal-like patients, and no successful models could be developed for ER-negative patients, or for the expression-defined HER2-enriched subtype. For most significant predictors, the Cox model contained human-derived modules, mouse-derived modules, and pathology variables; for example the predictor for ER-positive patients contained human modules for proliferation, hypoxia, an embryonic stem cell derived signature, three mouse modules with one containing multiple metalloproteinases, tumor size and node status. In addition, the actual modules selected for the different patient subsets varied such that the modules selected for the Basal-like patients did not overlap any module selected for all patients, or for the ER-positive patient subset.
 Conclusions: These analyses further reinforce the idea that breast cancer is a heterogeneous disease and suggest that prognostic models for each important patient subset should be developed and used. Models that contain treatment variables, in addition to prognostic terms, are also being developed and will be presented. In summary, these analyses argue that the best prognostic algorithms for breast cancer patients contain biological variables coming for humans, from model systems, and from classic pathology. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 5063.

Spike train signatures of retinal ganglion cell types

The mammalian retina deconstructs the visual world using parallel neural channels, embodied in the morphological and physiological types of ganglion cells. We sought distinguishing features of each cell type in the temporal pattern of their spikes. As a first step, conventional physiological properties were used to cluster cells in eight types by a statistical analysis. We then adapted a method of P. Reinagel et al. (1999: J. Neurophysiol., 81, 2558-2569) to define epochs within the spike train of each cell. The spike trains of many cells were found to contain robust patterns that are defined by the (averaged) timing of successive interspike intervals in brief activity epochs. The patterns were robust across four different types of visual stimulus. Although the patterns are conserved in different visual environments, they do not prevent the cell from signaling the strength of its response to a particular stimulus, which is expressed in the number of spikes contained in each coding epoch. Clustering based on the spike train patterns alone showed that the spike train patterns correspond, in most but not all cases, to cell types pre-defined by traditional criteria. That the congruence is less than perfect suggests that the typing of rabbit ganglion cells may need further refinement. Analysis of the spike train patterns may be useful in this regard and for distinguishing the many unidentified ganglion cell types that exist in other mammalian retinas.

Cell-type specific gene expression profiles of leukocytes in human peripheral blood

BackgroundBlood is a complex tissue comprising numerous cell types with distinct functions and corresponding gene expression profiles. We attempted to define the cell type specific gene expression patterns for the major constituent cells of blood, including B-cells, CD4+ T-cells, CD8+ T-cells, lymphocytes and granulocytes. We did this by comparing the global gene expression profiles of purified B-cells, CD4+ T-cells, CD8+ T-cells, granulocytes, and lymphocytes using cDNA microarrays.ResultsUnsupervised clustering analysis showed that similar cell populations from different donors share common gene expression profiles. Supervised analyses identified gene expression signatures for B-cells (427 genes), T-cells (222 genes), CD8+ T-cells (23 genes), granulocytes (411 genes), and lymphocytes (67 genes). No statistically significant gene expression signature was identified for CD4+ cells. Genes encoding cell surface proteins were disproportionately represented among the genes that distinguished among the lymphocyte subpopulations. Lymphocytes were distinguishable from granulocytes based on their higher levels of expression of genes encoding ribosomal proteins, while granulocytes exhibited characteristic expression of various cell surface and inflammatory proteins.ConclusionThe genes comprising the cell-type specific signatures encompassed many of the genes already known to be involved in cell-type specific processes, and provided clues that may prove useful in discovering the functions of many still unannotated genes. The most prominent feature of the cell type signature genes was the enrichment of genes encoding cell surface proteins, perhaps reflecting the importance of specialized systems for sensing the environment to the physiology of resting leukocytes.