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A brief interview with Dr. Mar Martinez Pastor

A brief interview with Dr. Mar Martinez Pastor

Dr. Mar Martinez Pastor is a microbiologist from Valencia, who currently works as a senior Research Scientist in the Schmid Lab (leader Dr. Amy Schmid) at Duke University. She is a specialist in microbial response to abiotic stress. At the Schmid lab her research is focused on the transcriptional regulation of iron homeostasis in halophilic archaea.

Halophilic archaea are salt-loving archaea, which can be found in hypersaline environments like the colorful salt pond pictured above in San Francisco Bay, California. Because halophilic archaea thrive in environments of extreme pH, temperature and salinity they are considered extremophiles. As the name suggests, studying how they cope under extreme conditions can also be extremely tricky and never boring.

In hypersaline environments iron availability can rapidly fluctuate. Thus, how different species of halophilic archaea control iron homeostasis relies on the role of certain transcription factors from the DtxR family that regulate the expression of hundreds of genes to facilitate the adaptation (Martinez-Pastor et al., 2017). To have an insight of the archaeal transcriptome changes as a consequence of the stress response, proper sequence coverage of mRNA is necessary. However, in prokaryotes the high rRNA:mRNA content (80-90% : ~10%) has been an obstacle in obtaining the desired information about the mRNA sequences.

In her latest article, Dr. Martinez compares and tests the efficiency of rRNA removal kits in the hopes of obtaining the “cleanest” mRNA sequences. Her results show the ribosome depletion kit from siTOOLs Biotech: Pan-Archaea riboPOOL was able to efficiently deplete >90 % of rRNA among Halobacterium salinarum (pictured left, image provided by Dr. Martinez), Haloferax mediterranei and Haloarcula hispanica. Likewise, the custom-design riboPOOL for the species Haloferax volcanii was highly successful in rRNA depletion (Martinez Pastor et al., 2022). 

In conclusion, we could say it’s the ideal time to study transcriptomics in extremophiles like salt-loving archaea. ??

Our Pan-Archaea riboPOOLs are ready, efficient and pleased to help “break” through the bottleneck in the study of genome-scale gene expression in archaea. We can’t wait to read what Dr. Martinez and her colleagues will find out next.

Lastly, besides learning about Dr. Martinez research we wanted to know more about her journey in science, her hobbies and what she enjoys. So here it goes:

Six questions for Mar (which means sea ? in Spanish):

1. What is the most interesting part of studying archaea?

Archaea are ancient microorganisms that colonize all kind of environments, from the most common to the weirdest. By shape and structure, they look like bacteria; however, there are some other features as the transcriptional machinery, that resembles to a simpler version of Eukaryotes. And even more, other traits make them to be unique (as their cell membrane structure). Using Archaea as a model organism makes me feel that I am studying the midpoint of life, and any discovery could be pointing in any direction, could explain evolution and adaptation, could be giving us insight from the past and lightening the future!

2. What is the most challenging part of studying iron homeostasis in halophilic archaeal species?

There is not a “starting point”! I started my scientific career investigating with the yeast Saccharomyces cerevisiae as a model organism, and every hypothesis was based on the bibliography, however, working with iron imbalance adaptation in Archaea I realized that different species, even those that are closely related, behave differently in response to iron stress! Also, I had to face many experiments that weren’t previously described in the bibliography (as for siderophore detection or for using kits as riboPOOLs for the first time!)

3. What drew you to study iron homeostasis?

I have been always curious to know more about how cells respond to abiotic stress. I am so thrilled to unravel the mechanism by which cells detect a change in the environment and trigger an adaptative response.

4. How important is it to have a mentor, and what advice do you give young scientist which are part of a lab that is not as supportive?

I was very lucky to join the Schmid lab. Dr. Schmid provides all the tools to learn science from different sides (wet biology, system biology, bioinformatics…), she is supportive and gives us plenty of opportunities to teach, to present our work in conferences and meetings, to attend courses and complement our formation, in summary, to grow as a complete scientist. Young scientists have more needs beyond learning technics. A mentor should be a model. My advice for young scientist is to learn as much as they can from their current mentor, but if this is not enough, to rush looking for the next one to learn from.

5. What would you do if you had more time?

In lab, long term experiments: growing cells for longer periods in changing conditions and check what transcriptional mechanisms they use to adapt. In life, I would like to get back to activities that I abandoned, or I do now with limited time. I would like to read novels, walk the dog or go swimming without thinking that every single minute that I am spending on a hobby is stolen from a “more important” activity!

6. Which is your favorite place in the world?



Featured image: Salt ponds with pink colored Haloarchaea on the edge of San Francisco Bay, California; photo by Kenneth Lu, 2013 available through Flickr.

Ribo-depletion in RNA-Seq – Which ribosomal RNA depletion method works best?

Ribo-depletion in RNA-Seq – Which ribosomal RNA depletion method works best?

Summary: This blogpost is focussed on ribosomal RNA (rRNA) depletion methods frequently applied to improve and economize RNA-Seq experiments.

The Rise of RNA-Seq

RNA-Seq Overtakes Microarrays

The use of Next-Generation RNA Sequencing (RNA-Seq) has recently overtaken that of DNA-based microarrays to detect and quantify changes in gene expression.

Why? RNA-Seq can detect novel coding and non-coding genes, splice isoforms, single nucleotide variants and gene fusions. Its broader dynamic range also enables sensitive detection of low abundance transcripts.

RNA-Seq vs Microarray

Also, technological advancements in single cell isolation, ribosome profiling and pulse-labelling techniques can now be multiplexed with RNA-Seq to widen the scope of scientific interrogation. Now, one can study the transcriptome, translotome and epitranscriptome with added spatial and temporal resolution. Studies of RNA structure and  RNA-protein/nucleic acid interactions with the use of nuclease digestion and biochemical pulldown approaches have also increasingly employed RNA-Seq. This excellent review describes all these latest advances.

A Major Challenge in RNA-Seq

A major limitation encountered in RNA-Seq however is the massive abundance of ribosomal RNA (rRNA) that can occupy up to 90% of RNA-Seq reads.  This necessitates additional steps for ribo-depletion or rRNA depletion to economize an RNA-Seq experiment.

Ribo-Depletion Methods

1) Poly-A selection

The most common method of rRNA depletion is poly-A selection, which relies on the use of oligo (dT) primers attached to a solid support (e.g. magnetic beads) to isolate protein-coding polyadenylated RNA transcripts. The main disadvantage though is one misses out on non-polyadenylated transcripts which include microRNAs, small nucleolar RNAs (snoRNAs), some long non-coding RNAs (lncRNA), and even some protein-coding RNAs (histones) which lack polyA tails. As a result, one fails to capture biologically relevant insights on these RNAs which make up a substantial proportion of the transcriptome.

Poly-A Selection - Advantages and Disadvantages

Curiously, polyadenylated transcripts are more abundant in eukaryotes as opposed to prokaryotes with both groups using polyadenylation in entirely different ways! Hence, polyA selection cannot be applied for sequencing of bacteria and archaebacteria, excluding its use in metagenomic RNA-Seq.

Poly-A selection also relies on transcripts being largely intact and tend to over-represent 3′ regions of transcripts. Studies comparing physical rRNA depletion methods and polyA selection show polyA selection did not work well for degraded RNA samples. A lower number of reads were also obtained with formalin-fixed paraffin-embedded (FFPE) tissues though analysis of fresh frozen tissues was not compromised.

Despite this, polyA selection still provides greater exonic coverage than physical rRNA depletion which tend to produce more intronic reads.  Further, a lower sequencing depth is typically needed for polyA selection, making it a respectable choice if one is focused only on protein-coding genes.

2) Physical Ribosomal RNA (rRNA) removal

Ribosomal rRNA can also be removed by hybridization to complementary biotinylated oligo probes, followed by extraction with streptavidin-coated magnetic beads. riboPOOLs developed by siTOOLs Biotech efficiently removes rRNA through this route, with a workflow similar to Ribo-Zero from Illumina.

Physical rRNA removal workflow
Workflow for rRNA removal with biotinylated probes and streptavidin-coated magnetic beads

Compared to polyA selection methods, rRNA removal enables detection of non-polyadenylated transcripts and small RNAs.  Comparisons between differential gene expression detected with both methods were typically well-correlated. The rRNA removal method however could detect both long and short transcripts showing less of a 3′ bias than polyA selection.

Physical rRNA removal also performs better for degraded and FFPE samples, and can also be applied for metagenomic samples that contain microbes. The Pan-Prokaryote riboPOOL by siTOOLs for example, functions effectively to remove rRNA from a diverse range of prokaryotic species, and can be used in combination with human and mouse/rat riboPOOLs to deplete rRNA from complex samples containing multiple species.

Physical rRNA Removal - Advantages and Disadvantages

By using targeted probes, one can also flexibly deplete abundant RNAs that take up expensive RNA-Seq reads. For example, globin, found in high amounts in RNA isolated from blood samples, can be efficiently depleted by globin mRNA-specific probes.

Ribosomal RNA can also be removed by selective degradation where enzyme RNase H is used to specifically degrade DNA-RNA hybrids formed between DNA probes and complementary rRNA (e.g. NEBNext rRNA depletion kit by New England Biolabs). This method was reported to produce consistent results, working as well on degraded samples though there was a bias against detecting transcripts of shorter length compared to Ribo-Zero.

3) Targeted amplification

An alternative method to deplete rRNA involves the use of  “not so random” hexamer/heptamer primers with a decreased affinity for rRNA during first strand cDNA synthesis. This is employed by the Ovation RNA-Seq kits from NuGen. Though the kit can be used to detect non-polyA RNAs and can be applied to prokaryotes, the additional incorporation of oligo(dT) still contributes to a bias towards  detecting 3′ regions.

A recent ribosome profiling study comparing library preparation methods reported fewer reads obtained and greater intronic reads for Nugen kits compared to polyA-selection methods. As Nugen also incorporated an RNase-mediated degradation of unwanted transcripts during final library construction steps, this indicates targeted amplification alone cannot totally remove rRNA. The method does however work with low input amounts and degraded samples.

 Targeted Amplification with not so random primers - Advantages and Disadvantages

So which ribo-depletion method works best?

And the answer as always? It depends. Depending on the ribo-depletion method chosen in RNA-Seq library preparation, some differences in genes detected and their expression levels will certainly be observed.

Poly-A selection might be the most efficient method when only focussed on protein-coding genes, but one loses significant information on non-polyadenylated RNAs and immature transcripts. In instances such as microbial sequencing or in sequencing degraded or FFPE samples, poly-A selection cannot even be applied or may perform poorly.

Physical rRNA removal offers the advantage of retrieving more transcriptomic information but comes at a cost of greater intronic/intergenic reads that necessitates a greater sequencing depth. However, it offers greater flexibility and better performance in sequencing of challenging sample types.

Targeted amplification with “not so random” primers though effective for low input material, comes also at a cost of greater sequencing depth required.

All methods are subject to some extent of non-specificity and detection bias. Further variability can also arise from different methods of sequence alignment in RNA-Seq data analysis. It is therefore always advisable to validate sequencing data obtained by real-time quantitative PCR (rtqPCR) or other methods.



1. Song, Y., Milon, B., Ott, S., Zhao, X., Sadzewicz, L., Shetty, A., Boger, E. T., Tallon, L. J., Morell, R. J., Mahurkar, A., and Hertzano, R. (2018) A comparative analysis of library prep approaches for sequencing low input translatome samples. BMC Genomics. 19, 696
2. O’Neil, D., Glowatz, H., and Schlumpberger, M. (2013) Ribosomal RNA Depletion for Efficient Use of RNA-Seq Capacity. in Current Protocols in Molecular Biology, p. 4.19.1-4.19.8, John Wiley & Sons, Inc., Hoboken, NJ, USA, 103, 4.19.1-4.19.8
3. Stark, R., Grzelak, M., and Hadfield, J. (2019) RNA sequencing: the teenage years. Nat. Rev. Genet. 10.1038/s41576-019-0150-2
4. Cui, P., Lin, Q., Ding, F., Xin, C., Gong, W., Zhang, L., Geng, J., Zhang, B., Yu, X., Yang, J., Hu, S., and Yu, J. (2010) A comparison between ribo-minus RNA-sequencing and polyA-selected RNA-sequencing. Genomics. 96, 259–265
5. Zhao, S., Zhang, Y., Gamini, R., Zhang, B., and von Schack, D. (2018) Evaluation of two main RNA-seq approaches for gene quantification in clinical RNA sequencing: polyA+ selection versus rRNA depletion. Sci. Rep. 8, 4781
6. Herbert, Z. T., Kershner, J. P., Butty, V. L., Thimmapuram, J., Choudhari, S., Alekseyev, Y. O., Fan, J., Podnar, J. W., Wilcox, E., Gipson, J., Gillaspy, A., Jepsen, K., BonDurant, S. S., Morris, K., Berkeley, M., LeClerc, A., Simpson, S. D., Sommerville, G., Grimmett, L., Adams, M., and Levine, S. S. (2018) Cross-site comparison of ribosomal depletion kits for Illumina RNAseq library construction. BMC Genomics. 19, 199


Featured Image is an artist’s rendition of a ribosome. Credit: C. BICKLE/SCIENCE


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