Spatially-Resolved Proteomics: Rapid Quantitative Analysis of Laser Capture Microdissected Alveolar Tissue Samples

Abstract

Laser capture microdissection (LCM)-enabled region-specific tissue analyses are critical to better understand complex multicellular processes. However, current proteomics workflows entail several manual sample preparation steps and are challenged by the microscopic mass-limited samples generated by LCM, impacting measurement robustness, quantification and throughput. Here, we coupled LCM with a proteomics workflow that provides fully automated analysis of proteomes from microdissected tissues. Benchmarking against the current state-of-the-art in ultrasensitive global proteomics (FASP workflow), our approach demonstrated significant improvements in quantification (~2-fold lower variance) and throughput (>5 times faster). Using our approach we for the first time characterized, to a depth of >3,400 proteins, the ontogeny of protein changes during normal lung development in microdissected alveolar tissue containing only 4,000 cells. Our analysis revealed seven defined modules of coordinated transcription factor-signaling molecule expression patterns, suggesting a complex network of temporal regulatory control directs normal lung development with epigenetic regulation fine-tuning pre-natal developmental processes.

Figure 1. High-throughput LCM-proteomics platform for ultrasensitive analysis.

Schematic of the LCM-proteomics workflow. Lung tissue is sliced and the microdissection is performed on a slice. The top panel is a representative LCM cut of the alveolar tissue from a sample obtained at postnatal day 7. The left image is the schematic of the cutout. The top-right image is the tissue leftover after the cutout; the bottom-right image is showing the tissue excised onto the LCM cap. The protein are then extracted from the microdissected tissue and injected onto the IMER column for on-line proteolysis. Contaminating substances are removed via on-line SPE trap and neat peptide is transferred on-line to C18 analytical column for MS analysis. “LCM-Prot; LCM-proteomics platform”.

Figure 2. Performance evaluation of high-throughput LCM-proteomics platform on mass-limited LCM samples.

(A) Chart showing number of identified proteins scales with number of cells represented in LCM sample. Increase in protein identifications begins to plateau after 2,000 cells. All the LCM cuts used for this figure were obtained from mice at the embryonic day 16.5. (B) Pearson’s correlation matrix demonstrating the reproducibility of the SNaPP platform at the protein level. Reproducibility was assessed utilizing 5 identical sample injections. The 5 replicate injections were performed from the same sample containing homogenate from 3 LCM cuts obtained at the post-natal day 7. The scatter plots representing the pairwise correlation plots for the proteins are shown in Fig. S2. (C–F) The comparison between FASP and our platform were performed in triplicate for each method and from LCM cuts obtained from mice at the post-natal day 28. (C) Percentage of identified peptides with measured intensity in all replicates for our platform and FASP. (D) Coefficient of variation for measured peptide abundance in our platform and FASP. (E) Percentage of identified proteins with measured intensity in all replicates for our platform and FASP. (F) Coefficient of variation for measured protein abundance in our platform and FASP. LCM-Prot; LCM-proteomics platform.

Figure 3. High-throughput LCM-proteomics platform enables effective comparative proteomics of mass-limited LCM samples.

(A) Venn diagram of proteins quantified in at least three out of five replicate in one of the three developmental ages (E16.5, PND7, PND28) examined. (B) Pearson Correlation matrix and hierarchical clustering of the samples based on the protein LFQ intensities. (C) Principal components analysis (PCA) of the LCM samples at the three developmental ages (E16.5, PND7, PND28) examined; the percentage in the parenthesis represents the percentage of variance explained by the first and the second Principal Component (PC1 and PC2).

Figure 4. Proteins and biological functions significantly changing during alveolarization.

The heatmap shows the 1,369 proteins that are changing in abundance over time (one-way ANOVA pvalue < 0.01). The color scale of the heatmap represents Z-scores of log₂(normalized intensities). K-means clustering algorithm was used to classify the proteins into six clusters depending on their temporal behavior. For each cluster, the NIH DAVID Bioinformatics Resource was used to perform functional enrichment of the Biological Function GOs. Manually curated enriched Biological Functions are represented.

Figure 5. Regulatory and signaling proteins mediating alveolar formation.

305 proteins significantly changing over time (one-way ANOVA pvalue < 0.05) and annotated as a transcription/translation regulator or signaling molecule in the curated Ingenuity Pathway Analysis (IPA) database were clustered in 7 temporal behavioral groups using K-means clustering algorithm. On the left, the colored lines represent the average Z-scores of the cluster centroid over time. The error bars represent the Standard Error; the grey lines are the average of each individual protein belonging to a given cluster. Names of transcription/translation regulators and signaling molecules present in each cluster are written in the corresponding table.

Figure 6. Known protein abundance patterns confirmed by LCM-proteomics platform during alveolarization.

(A) Representation of abundance evolution for known transcription factors involved in lung development. Nkx2-1: highest expression at E16.5; Hopx: highest expression at PND28; Smad2: highest expression at E16.5. (B) Surfactant proteins and surfactant maturation associated proteins abundance at E16.5, PND7 and PND28; the errors bars represent the standard error.