Cell-type-specific transcriptomics in chimeric models using transcriptome-based masks - PubMed (original) (raw)

Cell-type-specific transcriptomics in chimeric models using transcriptome-based masks

Felix Naef et al. Nucleic Acids Res. 2005.

Abstract

Regulatory networks involving different cell types control inflammation, morphogenesis and tissue homeostasis. Cell-type-specific transcriptional profiling offers a powerful tool for analyzing such cross-talk but is often hampered by mingling of cells within a tissue. Here, we present a novel method that performs cell-type-specific expression measurements without prior cell separation. This involves inter-species transplantation or chimeric co-culture models among which the human mouse system is frequently used. Here, we exploit the sufficiently divergent transcriptomes of human and mouse in conjunction with high-density oligonucleotide arrays. This required a masking procedure based on transcriptome databases and exhaustive fuzzy mapping of oligonucleotide probes onto these data. The approach was tested in a human-mouse experiment, demonstrating that we can efficiently measure species-specific transcriptional profiles in chimeric RNA samples without physically separating cells. Our results stress the importance of transcriptome databases with accurate 3' mRNA termination for computational prediction of accurate probe masks. We find that most human and mouse 3'-untranslated region contain unique stretches to allow for an effective control of cross-hybridization between the two species. This approach can be applied to xenograft models studying tumor-host interactions, morphogenesis or immune responses.

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Figures

Figure 1

Figure 1

Raw and masked expression signals. (a) and (b) compare expression signals of a 1:1 human colon (HC) and mouse liver (ML) mixture versus pure HC RNA. +/− 2-fold lines are indicated in green. (a) Standard RMA algorithm16 without masking or normalization. (b) Masked and normalized data. Oligonucleotide probes with three or fewer MMs to a mouse transcript in RefSeq or tromer were masked, and PSs with <4 probes left were discarded.

Figure 2

Figure 2

Oligonucleotide probe masks. (a) Distribution of probes per probe set (PS) of the Human Genome U133 Plus 2.0 array after masking all probes onto the mouse transcriptome with a fixed maximal number of MMs. (b) Masking efficiency. Outlier counts according to Figure 1b as a function of log-ratio thresholds. ‘U’ is the unmasked results, ‘0’ masks only probes with perfect matches, ‘1’ with up to one mismatch, etc. (c) Number of probes in the coding (C) or non-coding (N) part of mRNAs after masking up to a given number of MMs (_x_-axis). The number of used or masked probes with 3 MMs in relation to its localization in the coding or UTR part of mRNAs is shown in the insert. Only probes with matches on RefSeq are considered. (d) Accuracy of expression signals for truncated PSs in the 50% HC + 50% ML versus 100% HC comparison. Analysis is stratified according to the number of probes (NP) left after masking. Only pairs with mean intensity >7 (unit and scale according to RMA output) are used. Boxplots show uniform behavior in function of NP, and tight correlation between diluted and undiluted signal estimates: 50% of the data are reproducible within a factor of 20.15∼1.1. (e) All individual probes belonging to the 100 most outlying PSs in Figure 1a are stratified according to the number of MMs to their predicted target and the longest perfectly matching stretch (s).

Figure 3

Figure 3

Assessment of human-specific differential expression after dilution with mouse RNA. All samples were masked and normalized as in Figure 1b. (a) (10% HC + 40% HH + 50% ML) versus (25% HC + 75% ML). Genes differentially expressed in heart (magenta) or colon (green) according to SAGE (P < 0.05). The cyan polyA spikes should fall onto the indicated 10-fold line (Table 1). (b) Compression induced by dilution. The density of log2 ratios is narrowest for 1:3 human:mouse mixture (green) and widens for smaller dilutions (red is 50%, and black 0% mouse). (c and d) Comparison of the expression changes for genes in the test set (green and magenta in panel a) in the pure human mixture (80% HH + 20% HX) versus the human mixture diluted 1:1 with mouse. (e and f) Fraction of positives recovered after addition of mouse RNA plotted against the FDR for the 1:1 (black) and 1:3 (red) human:mouse mixtures. Here, ‘positives’ were defined as the 2% most induced or repressed genes in the undiluted 20% HC 80% HH versus 100% HC comparison.

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