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. 2016 Jul 8:7:12139.
doi: 10.1038/ncomms12139.

Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling

Susanne Nichterwitz  1 , Geng Chen  2 , Julio Aguila Benitez  1 , Marlene Yilmaz  2 , Helena Storvall  2   3 , Ming Cao  1 , Rickard Sandberg  2   3 , Qiaolin Deng  2 , Eva Hedlund  1
Affiliations

Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling

Susanne Nichterwitz et al. Nat Commun. .

Abstract

Laser capture microscopy (LCM) coupled with global transcriptome profiling could enable precise analyses of cell populations without the need for tissue dissociation, but has so far required relatively large numbers of cells. Here we report a robust and highly efficient strategy for LCM coupled with full-length mRNA-sequencing (LCM-seq) developed for single-cell transcriptomics. Fixed cells are subjected to direct lysis without RNA extraction, which both simplifies the experimental procedures as well as lowers technical noise. We apply LCM-seq on neurons isolated from mouse tissues, human post-mortem tissues, and illustrate its utility down to single captured cells. Importantly, we demonstrate that LCM-seq can provide biological insight on highly similar neuronal populations, including motor neurons isolated from different levels of the mouse spinal cord, as well as human midbrain dopamine neurons of the substantia nigra compacta and the ventral tegmental area.

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Figures

Figure 1
Figure 1. Schematics of Smart-seq2 coupled with LCM (LCM-seq) for analysis of single neurons.
Spinal cords isolated from mouse pups were sectioned at 12 μm thickness at cervical and lumbar levels. MNs were visualized using Histogene staining, 120, 50, 30, 10, 5, 2 and 1 cell captured by a Leica LMD7000 system and collected in PCR tubes. Cells were lysed, cDNA synthesized, amplified and analysed using a Bioanalyzer and sequencing libraries subsequently prepared. The cDNA profile is exemplified by a Bioanalyzer profile of 50 captured MNs. bp, basepairs; FU, fluorescent units.
Figure 2
Figure 2. LCM-seq improved the sensitivity of gene detection and is applicable to single cells.
(a) Reproducibility of detected genes was evaluated by comparing all possible pairwise comparisons within replicates of each group (control RNA extraction and 120, 50, 30, 10, 5, 2 and 1 LCM-seq samples), shown as mean with 90% confidence interval of reproducible ratios. LCM-seq improved reproducibility of detection of low and medium level expressed genes compared to the RNA extraction protocol (RNA extraction) (mean±s.e.m., n⩾4). (b) Standard deviation of gene expression within replicates binned according to gene expression levels. LCM-seq samples of 120–30 cells showed reduced technical variation for low and medium level expressed genes compared with the RNA extraction protocol (RNA extraction) (mean±s.e.m., n⩾4). (c) Mean number of genes detected in the different sample groups (mean±s.e.m.). A larger number of genes were detected in the 120 cell samples subjected to LCM-seq compared to the group subjected to RNA extraction before sequencing (0.1 RPKM as cutoff, P=0.04, Student's t-test). (d) Gene expression correlation was high for 120, 50, 30 and 10 cell samples, while 5, 2 and 1 cell samples showed the heterogeneity among spinal MNs (Spearman's correlation, genes expressed ⩾1 RPKM in at least one sample were used). (e) PCA for all groups based on top 500 variable genes in expression confirmed the high similarity between 120 and the RNA extraction group as well as with 50, 30 and 10 cell LCM-seq samples.
Figure 3
Figure 3. LCM-seq revealed the unique identities of cervical and lumbar spinal MNs.
(a) PCA for cSC (n=4 mice) and lSC (n=5 mice) MNs (120 neurons/sample) based on the top 500 variable genes in expression showed that the two groups separated along the 1st component. (b) Genes (899) were differentially expressed between cSC MNs and lSC MNs (adjusted P<0.05, Wald test). Heatmap shows the Z-score of each gene, which was calculated based on log10-transformed RPKM values. (c) cSC MNs and lSC MNs showed unique Hox gene expression profiles. (d) The top 20 differentially expressed genes between cSC and lSC neurons included eight Hox genes, and for example, Npy, Fstl1 and Sncb (sorted by fold change and adjusted P, shown as mean±s.e.m.). (e) The top 20 differentially expressed transcription factors (TFs) between cSC and lSC neurons included twelve Hox genes and for example, Nr2f2 and Zfhx4 (sorted by fold change and adjusted P, shown as mean±s.e.m.).
Figure 4
Figure 4. LCM-seq is applicable to partly degraded human post-mortem tissues and reveals differential gene expression of spinal MNs and mDA neurons.
(a) Mean number of genes detected in MNs (n=6) and mDA neurons (SNc: n=4; VTA: n=3, mean±s.e.m.). (b) Clustering of MNs (n=6) and mDA neurons (n=7) based on the top 500 variable genes in expression. (c) Top 20 differentially expressed genes between spinal MNs and mDA neurons (adjusted P<0.05, Wald test, DESeq2, sorted by fold change and adjusted P, mean±s.e.m.). (d) A larger number of genes was detected in mDA neurons using Histogene staining than the quick TH antibody staining (P=0.03, Student's t-test at 0.1 RPKM cutoff, mean±s.e.m.). (e) Comparable mDA neuron marker expression levels were detected between the Histogene and quick TH staining groups. Boxes range from the 25th to the 75th percentile, with the centerline representing the 50th percentile. Outliers are shown as dots. (f) Clustering of SNc (n=4) and VTA (n=3) neurons, using Histogene staining, based on the top 500 variable genes in expression. (g) The top 20 differentially expressed genes between SNc and VTA neurons (adjusted P<0.05, Wald test, DESeq2, sorted by adjusted P, mean±s.e.m.).
Figure 5
Figure 5. LCM-seq is applicable to single human MNs isolated from post-mortem tissues.
(a) Human MNs expressed the MN-specific markers ISLET-1/2, MNX1, CHAT, NEFH and PRPH. (b) Mean number of genes detected in the distinct groups of different cell numbers; bulk (179.7±30.8 cells), 10, 5 and 1 MN (displayed as mean±s.e.m.). (c) Gene expression correlation for bulk, 10, 5 and 1 cell samples (Spearman's correlation; genes expressed at ⩾1 RPKM in at least one sample were used). (d) PCA of bulk, 10, 5 and 1 MN groups and bulk mDA neurons, based on the top 500 variable genes.
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References

    1. Hedlund E., Karlsson M., Osborn T., Ludwig W. & Isacson O. Global gene expression profiling of somatic motor neuron populations with different vulnerability identify molecules and pathways of degeneration and protection. Brain 133, 2313–2330 (2010). - PMC - PubMed
    1. Chung C. Y. et al.. Cell type-specific gene expression of midbrain dopaminergic neurons reveals molecules involved in their vulnerability and protection. Hum. Mol. Genet. 14, 1709–1725 (2005). - PMC - PubMed
    1. Simunovic F. et al.. Gene expression profiling of substantia nigra dopamine neurons: further insights into Parkinson's disease pathology. Brain 132, 1795–1809 (2009). - PMC - PubMed
    1. Saxena S., Cabuy E. & Caroni P. A role for motoneuron subtype-selective ER stress in disease manifestations of FALS mice. Nat. Neurosci. 12, 627–636 (2009). - PubMed
    1. Murray L. M., Beauvais A., Gibeault S., Courtney N. L. & Kothary R. Transcriptional profiling of differentially vulnerable motor neurons at pre-symptomatic stage in the Smn (2b/−) mouse model of spinal muscular atrophy. Acta Neuropathol. Commun. 3, 55 (2015). - PMC - PubMed

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