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, 19 (6), 636-644

Re-evaluating Microglia Expression Profiles Using RiboTag and Cell Isolation Strategies

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Re-evaluating Microglia Expression Profiles Using RiboTag and Cell Isolation Strategies

Zhana Haimon et al. Nat Immunol.

Abstract

Transcriptome profiling is widely used to infer functional states of specific cell types, as well as their responses to stimuli, to define contributions to physiology and pathophysiology. Focusing on microglia, the brain's macrophages, we report here a side-by-side comparison of classical cell-sorting-based transcriptome sequencing and the 'RiboTag' method, which avoids cell retrieval from tissue context and yields translatome sequencing information. Conventional whole-cell microglial transcriptomes were found to be significantly tainted by artifacts introduced by tissue dissociation, cargo contamination and transcripts sequestered from ribosomes. Conversely, our data highlight the added value of RiboTag profiling for assessing the lineage accuracy of Cre recombinase expression in transgenic mice. Collectively, this study indicates method-based biases, reveals observer effects and establishes RiboTag-based translatome profiling as a valuable complement to standard sorting-based profiling strategies.

Conflict of interest statement

Competing Financial Interests

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. RiboTag analysis reveals that Cx3cr1Cre mice but not Cx3cr1CreER animals display rearrangements in neurons.
(A) Scheme of Cx3cr1Cre and Cx3cr1CreER systems. (B) Scheme describing the immuno-precipitation protocol, including brain homogenization, centrifugation to remove cell debris and incubation with magnetic beads and relevant antibodies. (C) Heat maps of RNAseq data comparing IPs obtained from brains of Cx3cr1Cre:Rpl22HA and Cx3cr1CreER:Rpl22HA mice, represented by lists of genes of microglia (115), neurons (97), astrocytes (95) and oligodendrocytes (98) showing enrichment and de-enrichment of mRNAs of specific cell types in the different samples. Reference data sets . Each column represents an individual mouse, n=2 for Cx3cr1CreER no TAM, n=3 for Cx3cr1Cre and Cx3cr1CreER with TAM. (D) Microscopic analysis of cortex brain sections from Cx3cr1Cre:R26-YFP mice (left panel) and Cx3cr1CreER:R26-YFP mice (TAM treated (right panel) or untreated controls (middle panel)), stained for IBA-1, YFP and DAPI, showing neuronal expression of YFP in Cx3cr1Cre brains and microglia-restricted YFP expression in Cx3cr1CreER brains. The animals analyzed are F1 offspring of the intercross of homozygote Cx3cr1CreER or Cx3cr1Cre animals and homozygote R26-YFP mice. Representative of 2 independent experiments. (E) Immuno-fluorescent staining of tissue sections of Cx3cr1Cre:Rpl22HA (left) and TAM-treated Cx3cr1CreER:Rpl22HA (right) mice, stained for IBA1, HA and Hoechst, showing neuronal expression of HA in Cx3cr1Cre cortex, and microglia-restricted HA expression in Cx3cr1CreER spinal cord. Scale bars: 200µm (left), 50µm (right) Representative of 2 independent experiments. (F) Flow cytometry analysis showing HA staining in microglia (CD11b+ CD45int, gated on Ly6C/G DAPI) of Rpl22HA TAM-treated mice (black line), Cx3cr1CreER:Rpl22HA mice, untreated (blue line) and TAM-treated (red line). Shadowed histogram represents isotype (IgG) control. Representative data of 3 repeats.
Figure 2
Figure 2. Comparison of cell sort-based protocol and the RiboTag method to profile microglia
(A) Scheme describing the experimental protocol comparing RiboTag and cell sort-based strategies. (B) Heat map of RNAseq data of samples obtained in (A). Genes selected by maximum value>100 normalized reads (3,186 out of 17,406 genes), significantly changed (fold change>2, p-value<0.05) between: IP-HA vs IP-IgG, Sort vs IP HA, Sort-IP vs IP HA and Sort vs Sort-IP, Representing 2,508 genes. n=3, individual mice. Statistical test was part of the DESeq2 package, using p-adjusted. (C) Heatmap representing K-means re-clustering of genes from cluster III from Figure 2B, showing genes high in IP-HA and low in Sort samples. (D) FACS dot plot (left panel) showing separation of microglia (CD45int) from other brain macrophages (MΦ) (CD45hi) by flow cytometry. Histogram (right panel) of microglia and MΦ isolated from Cx3cr1GFP mice indicating high CX3CR1/GFP expression in both populations. Representative of 3 independent experiments. (E) FACS histogram of HA staining in microglia (left panel) and other brain macrophages (MΦ) (right panel) in control Rpl22HA mice (grey) or TAM-treated Cx3cr1CreER:Rpl22HA mice (blue/red). Representative of 2 independent experiments. (F) Heatmap of RNA-seq data of representative non-parenchymal brain macrophages genes, showing enrichment in IP-HA, but not in sorted samples. (G) Graphs showing normalized reads of example genes from Figure 2F. Each dot represents an individual mouse, n=3, line represents mean. (H) Graphs showing normalized reads of example genes from cluster III-b and III-c in Figure 2C, showing functional genes enriched in IP and Sort-IP. Each dot represents an individual mouse, n=3, line represents mean.
Figure 3
Figure 3. Microglia isolation results in cell activation.
(A) Heatmap representing K-means re-clustering of genes from cluster II from Figure 2B, showing mRNAs high in Sort and low in IP-HA samples. (B) Graphs showing normalized reads of example genes from cluster II-a in Figure 3A, showing high level expression of immune-activation-related genes in sorted samples. Each dot represents an individual mouse, n=3, line represents mean. (C) Ingenuity Pathway Analysis (IPA) of genes significantly higher (>2 fold change, p-value<0.05, according to DESeq2 statistical analysis) in Sort compared to IP-HA (n=3), showing activated pathways related to immune response represented by activation Z-score as calculated by IPA software.
Figure 4
Figure 4. Microglia transcriptomes, but not translatomes include cargo-derived mRNAs and mRNAs sequestered in nuclei and P bodies
(A) Venn diagram of overlapping genes of cluster II-b (Figure 2C) (yellow) with genes of non-microglial cells, as selected according to . Non-microglia genes were selected by being with max value > 10, microglia reads < 30 and max value is > 3 fold change over microglia. Genes fitting these criteria represent cargo of ingested cells. (B) Graphs showing normalized reads of example genes from list of shared genes from Figure 4A, showing high levels cargo contamination in Sort samples, but not in IP. Each dot represents an individual mouse, n=3 (upper panels); Expression of genes above in different brain cells, data obtained from (lower panels). FKPM, fragments per kilobase of exon per million reads mapped. (C) Graphs showing normalized reads of example genes of long non coding RNAs that reside within the nucleus and are presented only in Sort but not IP samples. Each dot represents an individual mouse, n=3, line represents mean. (D) Violin plot representing splicing efficiency (left panel) and gene length (right panel) of genes in cluster II-b (orange) compared to genes not in II-b (green), showing that genes in cluster II-b are less efficiently spliced and have longer genes and longer 3’UTRs compared with the rest of genes in the dataset, suggesting nuclear retention. Splicing efficiency was computed by comparing intron-spanning and intron-crossing reads 28. Statistics was performed using Wilcoxon test, FDR correction was performed for right panel. * Splicing efficiency p=4.124*10-7, exon length p=6.636*10-63, Intron length p=1.575*10-27, UTR length p=1.865*10-35. (E) Violin plot representing cellular localization of genes in other tissues (left panel Liver, right panel – MIN6 pancreatic beta cell line) with established nuclear and cytoplasmic fractions. Genes in cluster II-b (orange) are more nuclear compared to other genes (green). Statistics was performed using Wilcoxon test and FDR correction. * Liver p=7.731*10-49, MIN6 p=2.980*10-17. n (number of genes) = 316 (Liver 2b), 1970 (Liver not-2b), 306 (MIN6 2b), 1846 (MIN6 not-2b). Liver and MIN6 datasets were based on 2 independent experiments . (F) Graphs showing normalized reads of immediate-early genes found in Sort but not in IP-HA (in cluster II-b, microglial genes), suggesting sequestration from translation in unsorted cells. Each dot represents an individual mouse, n=3, line represents mean. (G) Diagram representing different states of mRNAs in the cell: nuclear retention, sequestration from translation in P-bodies or active translation in the ribosomes.
Figure 5
Figure 5. Analysis of microglia isolated from mice challenged with LPS
(A) Venn diagram of overlapping genes upregulated by LPS treatment in IP-HA (blue) and in Sort (yellow), showing 54 genes upregulated in IP-HA only, 247 genes shared between methods and 214 genes upregulated by sorting only. (B) Correlation analysis of 247 shared genes (from Figure 5A) upregulated due to LPS in both methods, representing log2 fold change of significantly changed genes (log2 fold change>1, pValue<0.05, as calculated by DESeq2 statistical analysis) in LPS vs PBS in each of the methods (left panel); Graphs showing normalized reads of example genes detected as upregulated with LPS by both methods (right panels). Each dot represents an individual mouse, n=3 in PBS group, n=4 in LPS group, line represents mean. For IP-HA genes were selected by first being enriched over control IP-IgG, and then by LPS>PBS. (C) Venn diagram of overlapping genes downregulated by LPS treatment in IP-HA (blue) and in Sort (yellow), showing 10 genes downregulated in IP-HA only, 104 genes shared between methods and 250 genes downregulated by sorting only. (D) Correlation analysis of 104 shared genes (from Figure 5C) downregulated due to LPS in both methods, representing log2 fold change of significantly changed genes (log2 fold change<-1, p-value<0.05, as calculated by DESeq2 statistical analysis) in LPS vs PBS in each of the methods (Left panel); Graphs showing normalized reads of example genes detected as downregulated with LPS by both methods (Right panels). Each dot represents an individual mouse, n=3 in PBS group, n=4 in LPS group, lines represent mean. For IP-HA genes were selected by first being enriched over control IP-IgG, and then by LPS<PBS. (E) Graphs showing normalized reads of example genes related to immune activation that were up- (upper panels) or down- (lower panels) regulated due to LPS treatment in sorted samples only but not detected in IP-HA, showing differential susceptibility of biologically treated samples to artifacts introduced by isolation method. Each dot represents an individual mouse, n=3 in PBS group, n=4 in LPS group, lines represent mean. (F) Graphs showing normalized reads of example genes originated from ingested cargo that were up- (upper panels) or down- (lower panels) regulated due to LPS treatment in Sort only but not detected in IP-HA or Sort-IP, showing that the whole translatome includes LPS-dependent changes that do not originate in microglia thus introducing false information. Each dot represents an individual mouse, n=3 in PBS group, n=4 in LPS group, lines represent mean.

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