A proteomics approach to decipher the molecular nature of planarian stem cells
- Enrique Fernández-Taboada†1,
- Gustavo Rodríguez-Esteban†1,
- Emili Saló1Email author and
- Josep F Abril1Email author
© Fernández-Taboada et al; licensee BioMed Central Ltd. 2011
Received: 8 November 2010
Accepted: 28 February 2011
Published: 28 February 2011
In recent years, planaria have emerged as an important model system for research into stem cells and regeneration. Attention is focused on their unique stem cells, the neoblasts, which can differentiate into any cell type present in the adult organism. Sequencing of the Schmidtea mediterranea genome and some expressed sequence tag projects have generated extensive data on the genetic profile of these cells. However, little information is available on their protein dynamics.
We developed a proteomic strategy to identify neoblast-specific proteins. Here we describe the method and discuss the results in comparison to the genomic high-throughput analyses carried out in planaria and to proteomic studies using other stem cell systems. We also show functional data for some of the candidate genes selected in our proteomic approach.
We have developed an accurate and reliable mass-spectra-based proteomics approach to complement previous genomic studies and to further achieve a more accurate understanding and description of the molecular and cellular processes related to the neoblasts.
As we move further into the post-genomic era it becomes increasingly clear that DNA sequence data alone is insufficient to explain complex cellular and molecular processes. Although the enormous volume of data generated by genome sequencing projects, expressed sequence tags (ESTs), and cDNA analyses has improved our understanding of many processes, they often fail to reflect the influence of posttranscriptional modifications and protein interactions or offer a true reflection of protein levels or activity. Consequently, the role of specific proteins is relatively difficult to determine with confidence on the basis of mRNA expression or genomic data alone [1, 2].
Proteomic approaches offer a more realistic description of protein function and its influence on cell dynamics. Although comparative analysis of phenotypically different biological samples, such as in diseased versus healthy tissue , remains a challenge, those studies raise the possibility of identifying the protein "signatures" that underlie key biological phenomena . Furthermore, the use of bioinformatics to integrate data obtained using genomic and proteomic techniques could help to bypass the limitations of each approach and achieve a more comprehensive view of the information flow within cells.
Establishment of the planarian proteomic approach
Variables taken into account for the establishment of the planarian proteomic protocol using 2D gels.
Whole planarian extracts,
dissociated cell extracts,
dissociated cell and sub-fractionated extracts.
Amersham 2D clean up kit,
(Immobiline Dry strip gels 24 cm)
Linear pH 4-7,
Linear pH 7-11,
Non-linear pH 3-11.
general protease inhibitors,
Spot counts for the 2D gels.
Final Selected Spots
1182 ± 43.13
901 ± 77.07
1931 ± 92.63
1413 ± 81.31
After GO assignment and the corresponding functional annotation of the sequences derived from our approach, enzyme codes were mapped by BLAST2GO when possible. With such codes it was possible to retrieve the KEGG pathway where the protein may play its role on the planarian molecular biology. However, less than one third of the sequences had a homologous gene/protein BLAST hit--especially for URFs dataset--, and from those many had a GO functional assignment. A fraction of the sequences with at least one GO hit was linked to an enzyme code, which would be related to a component of the KEGG pathways: 1,670 of 2,804 clusters, mapping to 118 pathways, and 131 of 5,528 clusters, mapping to 35 pathways, for MASCOT results on RefSeq and URFs respectively. All 35 pathways for URFs were also found using the RefSeq dataset. The lower ratio for the URFs set can be explained by species specific sequences, proteins or functions that are not yet annotated on the reference databases. 297 RefSeq clustered sequences had a match to 171 enzyme codes for proteins distributed on the 118 pathways. 16 URFs clustered sequences had a match to 9 enzyme codes for proteins distributed on the 35 pathways. The enzymes can appear on several pathways, due to the hierarchical structure of the KEGG a match can be found on both, a general route as "Metabolic pathway", and a more specific process, such as "Glycolysis/Gluconeogenesis". Among the pathways found, metabolism routes of sugars and lipids were expected, as energy is required for cellular processes, regeneration among them. Nevertheless, there are few candidate sequences that will deserve further analyses, as they appear on pathways close to development and regeneration: "Selenoamino acid metabolism", "Retinol metabolism in animals", and "mTOR signaling pathway". Additional data, including figures of all those pathways with color-highlighted boxes for proteins found, is available on the planarian proteomics web page .
As depicted in Figure 5, the annotated proteins cover a wide range of biological processes, of which four main groups can be emphasized: proteins involved in energy production and metabolism (red dots in Figure 5); gene expression and transcription regulators (yellow squares); proteins related to development and differentiation (blue diamonds); and proteins involved in stress-response pathways and the apoptosis (purple stars). This functional distribution resembles the distributions described in previous studies of embryonic stem (ES) cells , proliferating cells , and differentiating neural stem cells , among others [28–30] (see corresponding table in Additional File 3). Additional protein sequence comparisons were performed using NCBI BLAST  (E-value < 10e-3) to extensively compare sets of candidate proteins from our RefSeq and URFs databases with the sequences described in those studies as stem-cell related. The same analysis was applied to the genes reported in two studies using high-throughput approaches to detect neoblast genes by RNAi-feeding  and by expression macrochip  (see corresponding table in Additional File 3). A total of 822 sequences out of 2801 (29.35%) from the RefSeq dataset and 50 out of 309 (16.18%) from the URFs dataset presented homology with at least one sequence in any of the studies. Yet only 52 (1.86%) from RefSeq and none from the URFs dataset had homology with sequences reported in the planarian studies.
Summary of BLAST hits found for the analyzed candidate sequences
RefSeq candidate sequences
Rab-11B, member RAS oncogene family
Rab-39, Ras-related protein
Hsp70 (Mortalin-like protein)
PrkC (cAMP-dependent protein kinase)
URFs candidate sequences
Chaperonin containing TCP1 theta subunit
Splicing factor 3b subunit 1
TNF receptor associated factor
Similar to pol polyprotein
Since neoblasts are known to be the only source of cells for homeostasis and regeneration, the relationship between the selected genes and the neoblasts was validated by RNAi experiments [48, 49]. All injected animals, both intact and regenerating, died within a few days or weeks, except in the case of Rab39 and Hunchback-like (Figure 6B and 6G), for which no phenotype was observed in RNAi experiments. Intact planarians showed a gradual head regression followed by lysis after several weeks, as shown in Figure 6C, D and 6H. This phenotype has been linked to a lack of neoblast cells available for cell renewal . In addition, regeneration was completely absent in fragments from RNAi-treated animals, which produced small blastemas that never differentiated, or no blastema at all with indented wounds, as illustrated in Figure 6A, E, F and 6I.
The results of this study show that we have successfully developed a rapid and reliable method for 2D analysis of planarian protein samples (Figure 2 and Additional files 1 and 2). This approach will provide the basis for future proteomics studies that will increase our understanding of a number of biological processes, in planarians and beyond, building upon data obtained using genomics and cDNA-based approaches.
Proteomic studies can help to fill gaps on the annotation of the planarian genome. Despite the large number of entries already submitted, sequence databases such as NCBI  or UniProt  are far from complete. Recent metagenomic projects have identified novel putative protein sequences not present in current sequence databases, thus extending the range of biological functions that may be represented . For instance, Yooseph et al  report up to 1 in 3 orphan ORFs from whole-genome shotgun sequencing of marine samples containing a mixture of prokaryotic organisms. Our findings indicate that MASCOT can assign substantially more peaks on those spots selected from 2D gels when using the Smed_URF database than with NCBI-nr/RefSeq, as would be expected.
The use of ORF sequences in whole genomes without prior knowledge of where the genes, mainly the exons, are located presents a number of issues that can distort the measures used to discriminate between true and false peptide hits. These include the ratio of coding to non-coding sequences, which can be quite low (around 2% of coding regions for the human genome ), and the presence of more repetitive sequences in intergenic regions, despite the fact that some amino acid repeats are vital functional and structural regions in proteins . Moreover, the experimental spectra are compared to simulated ones that were computed from putative protein-coding regions directly translated from genomic sequences of the same species, not from related homologs from different organisms at different phylogenetic distances.
Galindo et al.  described a novel family of eukaryotic coding genes consisting of peptides shorter than 50 amino acids (small ORFS [smORFs]) with key biological functions during Drosophila development. Therefore, future searches will have to take this into account, for instance removing any length constraint when building up the ORF databases.
Identification of proteins
Apart from the presence of metabolic proteins that indicate the high metabolic rate of neoblasts, several of the proteins detected in this analysis seem to be good candidates to be involved in neoblast-related functions, and thus in regeneration and tissue homeostasis. One of those, Smed-SmB, from the LSm family, has been analyzed in detail and shown to be essential for neoblast proliferation and maintenance . Moreover, other candidates belonging to the HSP class of proteins have been linked to the biology of neoblasts in recent studies [59–61]. The experimental results described in this paper support the use of an ORF database built upon genomic sequences from the same species, which yields, as one might expect, more reliable results in subsequent proteomic searches, despite assuming nothing about the coding content of those ORFs. This will bridge the gap between proteomic and genomic approaches to extend our knowledge of the functional components of emerging model organisms.
An initial proteomic picture of the neoblasts
The genes identified in this study represent the first list of neoblast-related candidate genes identified using a proteomic approach in planarias (Table 3 and Additional file 4). The results show little correspondence to those of previous genomic studies [32, 33]. Interestingly, however, a number of the genes reported in this analysis were also present in studies designed to identify stem cell-specific genes in other model organisms [25–30]. In addition, five of the neoblast-related genes characterized through our proteomic approach (Hsp40, Hsp60, Hsp70, Chaperonin containing TCP1 theta subunit and Splicing factor 3b subunit 1) have also been analyzed in a planarian transcription macrochip, but only one of them was detected (Hsp60) . These findings support our proteomic strategy as a complement to genomic approaches. Furthermore, the large number of putative neoblast-related proteins identified in this proteomic study will be of invaluable help in future research investigating the biology of the neoblast.
We have developed a proteomic approach to characterize specific planarian stem-cell (neoblast) proteins. An accurate and reproducible method for protein purification, 2D gel electrophoresis and MS analysis was defined and an ORF database of species-specific genomic DNA was developed for peptide assignment of the retrieved MS spectra. Subsequent computational analyses yielded a list of annotated candidate proteins, some of which were functionally validated as neoblast-specific genes by RNAi and whole-mount in situ hybridization. Substantial overlap was observed between the candidate genes identified in our study and those reported from previous analyses of embryonic stem cells, thus validating the specificity of the approach. In addition, we detected novel sequence candidates and expression changes that merit further investigation in future studies to determine their role in stem-cell biology.
The genome of S. mediterranea (strain S2F2) was sequenced and assembled at the Genome Sequencing Center (GSC) at Washington University in Saint Louis (WUSTL) [62, 63]. It contains around 800 Mbp distributed on four chromosomes (2n = 8). The latest assembly version, v3.1 , comprises up to 90,000 sequences, which were reduced to 45,000 by means of pair-ends sequencing. Lengths of those sequences range from thousands to hundreds of thousands of nucleotides. During the assembly process, sequencing errors can be fixed by aligning different traces, but the software can also reduce polymorphisms and misplace those trace sequences because of the repeats. In order to overcome those limitations, a database of ORFs was produced directly from the set of the whole-genome shotgun reads. About 16 million traces were downloaded from the NCBI Trace Archive  and translated, without prior masking, into the six possible reading frames, taking into account only those ORF sequences longer than at least 50 amino acids. The ORFs were stored in a MySQL relational database along with the original sequences, to make it possible to retrieve the original nucleotide sequences and design probes for experimental validation. To reduce the large amount of sequence data produced and thus speed up the peptide searches by MASCOT , a set of URFs was derived from the set of ORFs with a checksum function to generate hash keys as unique identifiers for every sequence. A total of 54,382,803 ORFs were retrieved from 16,580,722 shotgun reads. This resulted in 28,946,081 URFs with properly formatted sequences to populate a MASCOT database. As MASCOT was not able to work with databases larger than 24 million entries, the original set was split into two databases. MASCOT results for both sets were then merged to get the final set of ORFs that had at least one peptide matching spectra. The probability of false matches increases when large databases, with millions of protein sequences, are used to detect a wide variety of possible candidate proteins in a sample [66, 67]. To assess the significance of the peptide hits found by MASCOT, a decoy database was built by reversing all the URF sequences [68–70]. It was also split into two, as described above for the "forward" database. MASCOT was run separately on the decoy databases for all the mass fingerprints previously analysed with the original URF dataset.
Intact asexual planarians were irradiated at 75 Gy (1,66 Gy/minute) with a Gammacell 1000 [Atomic Energy of Canada Limited] .
Protein samples were obtained from whole animals using a lysis buffer and heating. See Additional File 1 for further details.
Running 2D gels
First-dimension isoelectric focusing was performed on immobilized pH gradient strips (24 cm, pH 3-10) using an Ettan IPGphor system. Second-dimension SDS-PAGE was performed by laying the strips on 12.5% isocratic Laemmli gels (24 × 20 cm) cast in low-fluorescence glass plates on an Ettan DALT system. Details of the procedure are available in the Additional File 1.
Gel spots were extracted and digested before analysis by MS. Then, MASCOT software (Matrix Science, London, UK) was used to search those spectra on different databases. All spectra were processed by PRIDE Converter software  and were submitted to the PRIDE database , project accession number is 15541. For details see Additional File 1. After careful selection of score thresholds for the predicted peptides (see the Results section for the values chosen and the final numbers of the filtered datasets), the sequences that allowed detection of the URFs were uploaded into BLAST2GO [22, 23]. This software tool facilitates high-throughput integration of sequence data, homology to related species via NCBI-BLAST  and functional annotations of DNA or protein sequences based on the Gene Ontology (GO) classification . MASCOT output files, selected peptide and protein sequences, as well as BLAS2GO results and KEGG summary, are available at the planarian proteomics materials web page .
Gene identifiers and corresponding forward/reverse primers (including nested primers). GU591870: F1.5'-TCTGGGATACTGCAGTCC-3', R1.5'-GATGGAATAATCGGTTGCG-3';GU591871: F1.5'-TTTTAATTGGTGATAGCATGG-3', R1.5'-CTTGACCTGCTGTATCCC-3';GU591872: F1.5'-TGTTGTTGGTGACGGAGC-3', R1.5'-GCACGAATTGCCTCATCG-3', R2.5'-TGTTCGGACAGTGATGGG-3';GU591873: F1.5'-GACTATTATTCAATATTAGG-3', R1.5'-TACCTCATATGCTTCAGCAA-3';GU591874: F1.5'-TTGCTGAAGATGTTGACGG-3', R1.5'-AGAGCGGTACCTCCTCC-3', R2.5'-ACCTCACTACTACCACCG-3';GU591875: F1.5'-GAGACAAGCTACCAAAGATGC-3', R1.5'-CATCCGTAACATCTCCAGCAAG-3';GU591876: F1.5'-AACAAATATCTGGAATGCCC-3', R1.5'-GCTTAAAATTTCCGCGGAG-3';GU591877: F1.5'-CAATATGGCTGAGGCAGC-3', R1.5'-CTGGAGTTCCACACATCG-3', R2.5'-TGGATGGGAAATTTGCTCC-3';GU562964: F1.5'-CAACACTTCAAGATGGTCG-3', R1.5'-TTGCACCAGTACCTGGCA-3';GU591864: F1.5'-CCCAGTTCTTTTCAAGGTTTAGAAG-3', F2.5'-CTGTCTTCCGAAATATCCAAGCATGC-3', R1.5'-CCAAAGATTTTGGAATTTACTGCCGTTCG-3', R2.5'-CTTTACCAACAGATTCTTCGTCACG-3';GU591865: F1.5'-GCTCATGCGCTTGGCATTCGTATTTG-3', F2.5'-CGTTTCTGAAGGCTGTGTGCAAATC-3', R1.5'-CAATGGTGTCCGCGCCTTGAGCAAC-3', R2.5'-CAATTGCTCCTCCAACCGAATGTC-3';GU591866: F1.5'-GCAACAGATGACCAACAATATAAAGG-3', F2.5'-CTAGAAACCAACAATTTTATAGCCAG-3', R1.5'-CTTGTCCGGCCTCTCTACTTC-3', R2.5'-GATTATCTTCTCGCAAGAATCCTTCTC-3';GU591867: F1.5'-CCAGCTTTCTCAACAAAGACGGGAC-3', F2.5'-GTTTCAACAGAATGCCGTTTGGAATTGC-3', R1.5'-CCGGAAAACATAAGATTGGCGCCGTC-3', R2.5'-GTTTCAAACCCTCAAACACGCTATTCG-3';GU591868: F1.5'-GCACTAGATCAAAAAATAGAAGTGTTAGC-3', F2.5'-CTCAAGAAATGGAGGAACCAAGATTGG-3', R1.5'-CGATCTACTTCTTCTACAATCTC-3', R2.5'-CTGTTTCGTCTTCTCTTGACACGTTC-3';GU591869: F1.5'-GGCTAGGTAAGTATTGGATAGATGG-3', F2.5'-GGAACTGGACGATGGGTTGATAG-3', R1.5'-CCAATTTGTGTAGGTCATTTTGCATCC-3', R2.5'-CCATCATTGAATGTCCATCTTCCAGTG-3'.
In situ hybridization
Digoxigenin-labeled RNA probes were prepared using an in vitro labeling kit (Roche). Whole-mount in situ hybridization was performed as described by Agata et al , with some modifications: proteinase K (20 μg/ml) treatment for 10 min; triethanolamine treatment was performed as described by Nogi and Levin ; hybridization at 55°C for 18 or 30 h; and final probe concentration of 0.07 ng/μl.
Double-stranded RNAs (dsRNA) were produced by in vitro transcription (Roche) and injected into the gut of the planarians as described in Sánchez-Alvarado and Newmark . Three aliquots of 32 nl (400-800 ng/μl) were injected on three consecutive days with a Drummond Scientific Nanoject injector (Broomall, PA). On the fourth or fifth day, some of the planarians were amputated while the rest were left intact. Control organisms were injected with water.
expressed sequence tags
- 2D gel:
difference in gel electrophoresis
phosphorylated histone H3
open reading frame
NCBI non-redundant (database)
Washington University in Saint Louis
high-scoring segment pair (BLAST)
Enzyme Code (KEGG)
embryonic stem cells
heat shock protein
central nervous system
Genomic sequence data was produced by the Washington University Genome Sequencing Center in St. Louis, although trace sequences to generate the URFs database were downloaded from NCBI Trace server. We would like to thank Dr. Roger Florensa for his help in the protein sample preparation and setting up the 2D-gel running conditions, and Dr. Eliandre Oliveira and all members of the proteomic facility at the Parc Científic de Barcelona for their help in the proteomic work and analyses. We thank all members of the Saló group for advice and critical reading of the manuscript and Dr. Iain Patten for editorial advice. We are also grateful to the reviewers of the earlier version of the manuscript for their helpful comments. This work was supported by grants BFU-2005-00422 and BFU2008-01544/BMC from the Ministerio de Educación y Ciencia, Spain, and grant 2009SGR1018 from AGAUR (Generalitat de Catalunya, Spain). JFA started this project as a Juan de la Cierva post-doctoral fellow. E.F.T. and G.R.E. received an FPI fellowship from the Ministerio de Ciencia y Cultura.
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