Project description:Human type 1 diabetes (T1D) arises through autoimmunity towards the insulin-producing pancreatic β cells and is modeled by the BioBreeding (BB) rat. Factors associated with islet autoimmunity are dilute and difficult to directly measure in the periphery. Therefore, we previously utilized microarray-based bioassay where human T1D sera were used to induce a disease-specific gene expression signature in unrelated, healthy peripheral blood mononuclear cells (PBMC). Here we report that sera of both spontaneously diabetic BB DRlyp/lyp and diabetes inducible BB DR+/+ rats induce inflammatory signatures in healthy PBMC of non-diabetic Brown Norway (BN) rats, while allogeneic BN sera does not. Consistent with their common T1D susceptibility, transcriptional signatures of both BB sub-strains share identity, including cytokines, immune receptors and signaling molecules. However, like the human T1D signature, the DRlyp/lyp signature, which is associated with progression to diabetes, is differentiated from that of the DR+/+ by induction of many IL-1 regulated genes. Addition of IL-1RA to cultures significantly ablates the DRlyp/lyp signature. Daily treatment of DRlyp/lyp rats with human recombinant IL-1RA significantly delays onset and sera of treated animals do not induce the characteristic IL-1 signature. Consistent with the presence of immune regulation in DR+/+ rats is induction of a signature showing negative regulation of the NFκB pathway. This study supports prior human investigations of serum that reflect disease processes associated with progression to T1D and add to a growing body of evidence implicating the balance of IL-1 and IL-1RA as key factors influencing the severity and outcome of inflammatory responses. Experiment Overall Design: The induction of gene expression was accomplished by culturing the PBMCs of healthy day180 BN donor rats for 6 h at 37°C in 5% CO2 with a) 20% autologous BN plasma(10 replicates), b) allogeneic BN plasma (15 replicates), c) allogeneic DRlyp/lyp plasma (day 60, 4 replicates), or d) DR+/+ plasma (day 60, 4 replicates). Healthy BN PBMCs were also cultured with d60 DRlyp/lyp serum supplemented with IL-1RA (4 replicates) as well as autologous BN serum supplemented with IL-1B (4 replicates), to respectively block or induce IL-1 mediated gene expression. To determine if IL-1RA-treatment of DRlyp/lyp rats would result in modulation of the DRlyp/lyp signature, additional DRlyp/lyp rats were treated with hIL-1RA (n=6) or PBS (n=6). Rats were treated for 10 days, beginning at d30, then serum was collected at d40 and used to induce gene expression in PBMCs isolated from healthy d180 BN rats. Thus, there are a total of 53 chips being the addition of all the sample conditions mentioned above.

Project description:Brain circuits are assembled from a large variety of morphologically and functionally diverse cell types. It is not known how the intermingled cell types of individual brain regions differ in their expressed genomes. Here we describe an atlas of cell type transcriptomes of the adult retina. We found that each adult cell type expresses a specific set of genes, including a unique set of transcription factors, forming a “barcode” for cell identity. Cell type transcriptomes carry enough information to categorize cells into corresponding morphological classes and types. Surprisingly, several barcode genes are eye disease-associated genes that we demonstrate to be specifically expressed not only in photoreceptors but also in particular retinal circuit elements such as inhibitory neurons as well as in retinal microglia. Our data suggest that distinct cell types of individual brain regions are characterized by marked differences in their expressed genomes. To test whether amplifications were linear, we examined the relationship between gene expression values and the amounts of RNA in a cell group. We pooled together two retinas from the Chrnb4-GFP (cone) and six retinas from the ChAT-Cre × Rosa26-LSL-RFP (starburst amacrine cell) mouse lines. Five mixtures of GFP:RFP cells (“titration mixtures”) were then FACS-sorted with the following cone-starburst compositions: 0:200, 50:150, 100:100, 150:50 and 200:0. The total number of cells in each mixture was 200. Five mixtures from different mice were sorted 3 times to give biological triplicates. Next 20 genes with the highest specificity ratio for cones and another 20 for starburst cells were chosen and linear curves were fitted to the data pairs of expression value:cone number or expression value:starburst cell number. For both cone- and starburst-enriched genes, gene expression values increased linearly with an increase in the number of cones or starburst cells in the mixtures. The linear correlation coefficients were independent of the extent to which genes were expressed in the pure cone and starburst cell groups. This suggests that A practical use of the finding that amplification is linear is to separate the transcriptome of a cell group that contains cells from different types into its cell-type components. Assuming, for example, a cell group (Group A) containing a mixture of two cell types, Type 1 and Type 2, as well as a futher cell group (Group B) containing only Type 1 (Supplementary Figure 10). We wish to determine the transcriptome of Type 2 but we have no cell group (Group C) that contains Type 2 only. Or more generally, assume a cell group (Group A) containing a mixture of cell types, Type 1, Type 2…Type N, as well as a further cell group (Group B) containing only Type 1. We wish to determine the mixed transcriptome of Group C that contains Type 2….Type N, without Type 1. This separation of cell-type transcriptomes is useful for two reasons. First, ~80% of mouse retinal cells are rods30 and it is expected that transcripts from rods will be present in the solution after retina dissociation, leading to rod contamination of the transcriptome of each cell group. We wished to eliminate rod contamination. Second, we found that cones in some mouse lines are labeled together with a type of amacrine cell and we needed to determine the amacrine cell transcriptome alone. Due to linearity, the following procedure can be used to separate Type 1 from the rest of the cell types in a mixture (Group A). A set of cell type-specific genes for Type 1 is determined. This may be known a priori or from the dataset itself. The ratio of the expression value in Group A to the expression value in Group B is calculated for each Type 1-specific gene. The mean ratio is the estimate of the number of Type 1 cells in Group A. Next, the transcriptome (the expression values of all genes, which we treat as a vector) of Group B is multiplied by the mean ratio and the result is subtracted from the transcriptome of Group A. All negative numbers are then removed from this “unmixed” transcriptome by setting them to zero followed by normalization with the Group B (Type 1) transcriptome using quantile normalization. The quantile normalized “unmixed” transcriptome forms the prediction of the transcriptome of Group C. Note that if Group A contains only two cell types, Type 1 and Type 2, then the transcriptome of Group C is the transcriptome of Type 2 cells. We have demonstrated and quantitatively evaluated this procedure for the cone:starburst mixtures described above. In addition to the titration mixtures, we also made three “test mixtures” consisting the following cone:starburst cell numbers: 157:43, 75:125 and 183:17. Cones served as the Type 1 cells and starburst cells as Type 2. In our experiment, we had one pure cone group (200:0) and one pure starburst cell group (0:200) as well as six mixtures (50:150, 100:100, 150:50, 157:43, 75:125 and 183:17. First, we predicted the mixture composition using different numbers of cone-specific genes. Since we chose 20 cone-specific genes, the use of single genes presents 20 possible ways of predicting the number of cones in the mixture. Using two genes for the prediction provides 190 possible ways of predicting the number of cones in the mixture. The number of possible predictions when using k genes is: 20!/((20-k)! k!). When more than one gene is used for the prediction, the prediction is the mean of the individual gene-based predictions. Error was calculated as the mean of the absolute values of the deviation from the cone cell numbers in the mixtures. The mean and standard deviation of the prediction error decrease with the use of increasing number of genes. With 10 genes used for the prediction, for example, the prediction error will be approximately 12±3% (s.d.), regardless of which 10 genes are chosen, as long as they are cell type-specific. We pooled together two adult retinas from the Chrnb4-GFP (cone) and six adult retinas from the ChAT-Cre × Rosa26-LSL-RFP (starburst amacrine cell) mouse lines. Five mixtures of GFP:RFP cells (“titration mixtures”) were then FACS-sorted with the following cone-starburst compositions: ChAT.200: cone 0, ChAT.150: cone 50, ChAT.100: cone 100, ChAT.50 : cone 150, ChAT.0: cone 200. The total number of cells in each mixture was 200. Five mixtures from different mice were sorted 3 times to give biological triplicates (indicated by _1, _2, _3). X1, X2 and X3 stand for the following ratios: X1: ChAT.43 : cone 157, ChAT.125 : cone 75, ChAT.17 : cone 183. Arc represents as internal batch control to compare with other gene arrays within our dataset.

Project description:Brain circuits are assembled from a large variety of morphologically and functionally diverse cell types. It is not known how the intermingled cell types of individual brain regions differ in their expressed genomes. Here we describe an atlas of cell type transcriptomes of the adult retina. We found that each adult cell type expresses a specific set of genes, including a unique set of transcription factors, forming a “barcode” for cell identity. Cell type transcriptomes carry enough information to categorize cells into corresponding morphological classes and types. Surprisingly, several barcode genes are eye disease-associated genes that we demonstrate to be specifically expressed not only in photoreceptors but also in particular retinal circuit elements such as inhibitory neurons as well as in retinal microglia. Our data suggest that distinct cell types of individual brain regions are characterized by marked differences in their expressed genomes. We assembled a library of 22 transgenic mouse lines in which each line had a group of retinal cells marked with fluorescent proteins. We built up the library with the purpose of having some mouse lines in which single retinal cell types and others in which combinations of types from a single class are labeled. The library had mouse lines with labeled cells representing each of the six retinal cell classes. Retinal cells were characterized by physiological recording and immunohistochemical staining. We isolated 200 fluorescent protein-labeled retinal cells (“cell groups”) from at least three different mice of each mouse line by fluorescence-activated cell sorting. The transcripts of each cell group of these biological triplicates were independently amplified in batches. Each batch contained an internal control cell group from the Arc-line. We performed gene expression analysis of 22 transgenic mouse lines. All experiments were performed in biological triplicates. Gene expression analysis was performed in 3 batches. Arc is the batch control (1st, 2nd, 3rd). B2 was repeated 3 times.

Project description:Brain circuits are assembled from a large variety of morphologically and functionally diverse cell types. It is not known how the intermingled cell types of individual brain regions differ in their expressed genomes. Here we describe an atlas of cell type transcriptomes of the adult retina. We found that each adult cell type expresses a specific set of genes, including a unique set of transcription factors, forming a “barcode” for cell identity. Cell type transcriptomes carry enough information to categorize cells into corresponding morphological classes and types. Surprisingly, several barcode genes are eye disease-associated genes that we demonstrate to be specifically expressed not only in photoreceptors but also in particular retinal circuit elements such as inhibitory neurons as well as in retinal microglia. Our data suggest that distinct cell types of individual brain regions are characterized by marked differences in their expressed genomes. To obtain insight into the developmental time course of adult cell type-specific gene expression, we followed gene expression in one amacrine cell group, Arc cells, for 20 postnatal days (P) starting at P1. Qualitative analysis of the expression time-course of cell type-specific genes revealed four patterns: increasing, decreasing or constant expression from P1 to P20, or a biphasic time-course (constant and increasing) with a switch at P9-P10. At this latter time point, bipolar cells begin to form synapses with amacrine and ganglion cells and the retina becomes light responsive. Pairwise correlations across the transcriptomes measured on each day exposed two major clusters, from P1 to P9 and from P10 to P20, which also suggested a transcriptional switch during the transition from P9 to P10. Thus, most adult cell type-specific genes are also expressed during development but at levels different to those in the adult. We performed gene expression analysis of one amacrine cell group, Arc, from P0 to P20 and for Starburst amacrine cells for the time point P3 and P18. All experiments were performed in biological triplicates. 1 retina was used for each time point and each sample of the biological triplicate.

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