The CQLS has experienced significant change in the last few years.  Throughout this we have increased CQLS staffing, developed new research collaborations, and continued CQLS services without interruption.  As we move forward the CQLS will be increasing community outreach and events including restarting the Fall Conference (November 22nd) and working with faculty committees and our users and community to direct the future of the CQLS.

The CQLS is composed of four interconnected functional groups:

CQLS Core Laboratory – Four laboratory staff run the services provided in Genomics Core, Shared Instrumentation Core, and Microscopy Core.  Recently the laboratory has been working with the Division of Research and Innovation to move CQLS into the new RELMS instrument billing system.

CQLS Research Computing HPC – Two CQLS staff run and maintain the CQLS HPC, the largest and most widely used computing cluster at OSU.  Recent upgrades outlined in this newsletter combine the CQLS HPC with other OSU HPC to create a consolidated OSU research computing cluster.

CQLS Bioinformatics and Data Science – Six CQLS staff consult on computational research and teach our computing and bioinformatics courses.  CQLS scientists consult on experimental design, grant proposals, research, and programming projects and are highly utilized in many labs across OSU.

CQLS Health Data Informatics – Led by Denise Hynes, the CQLS provides support and tools for analyzing health data including protected and secure data.

In this newsletter you will find information on CQLS changes and new services, research projects in the CQLS, spotlight on CQLS service, and information on upcoming CQLS events.

Brent Kronmiller

CQLS Interim Director

Ken Lett, CQLS

After over a year of planning and hard work, CQLS is completing the upgrade of its entire high-performance computing (HPC) infrastructure to create a consolidated campus-wide HPC and provide accessible research computing across OSU.

The old GENOME cluster is being upgraded and replaced by an improved infrastructure with new features and across-the-board updates. These upgrades include new operating systems, Rocky 9 or Ubuntu 22, many software package updates, and improved resource management.

The Wildwood cluster uses SLURM as its primary job queueing system, which provides priority queuing and GPU-aware resource management. Wildwood also has SGE job management, to support those who rely on the SGE work flow for their analysis. To support multiple job management tools, we have also developed a suite of new tools (hpcman) that allow users to submit jobs and manage their work on SLURM or SGE with a unified command set.

One of the other big improvements in Wildwood is that we can now use ONID authentication – users no longer have to maintain a separate CQLS password, they can log in using their ONID password.

Wildwood is also a federated cluster – Wildwood connects to COEAS computing resources, and will soon also connect to the COE HPC. A single log in will allow users to run jobs and manage their data on any of
these clusters, and shared storage provides a unified interface to research data.

The CQLS Wildwood HPC contains 9PB of data storage, ~6500 processing CPUs, and 80+ GPUs.

Labs and departments have been making the transition to Wildwood through the summer, but Wildwood also has an expanded ‘all.q’ – general access resources anyone can use, including GPUs and PowerPC architecture machine.

For more information about the new cluster and how to connect, see the documentation webpage, the account request form, or contact CQLS HPC support: cqls-support@cqls.oregonstate.edu

Anne-Marie Girard, CQLS

CQLS has installed a new Leica Stellaris 5 Confocal Microscope System which replaces an older confocal system. With this type of microscope one can obtain 3D sectioning of fluorescently labeled cells, or tissues for clearer, sharper images of specimens. People have used confocal systems to examine structures within living or fixed cells and to examine the dynamics of cellular processes.

3D rendering of veins in maize leaves. Yellow: Pin1a-YFP in cell membrane, Red: DR5-RFP in endoplasmic reticulum. Image courtesy of Camila Medina.

A confocal system has the capacity to image in Z and time to better visualize location in 3D than widefield fluorescence microscope by using a pinhole to eliminate out of focus light. The system has a white light laser (WLL) with tunable excitations from 485 nm up to 685 nm in addition to a 405 nm laser and sensitive HyD S detectors with a detection range from 410 to 850 nm.  Additionally, the Stellaris system also has TauSense, a set of tools based on fluorescence lifetime information with potential to eliminate autofluorescence, and LIGHTNING which expands the extraction of image details for both classical imaging range and beyond the diffraction limit (120nm).

We will be offering free training and imaging time during this fiscal year to those people who have a project ready for imaging and in order to help with grant writing for future imaging projects. Contact Anne-Marie Girard to discuss potential projects or for more information about the system or its capacities.

The Division of Research and Innovation is combining Center and Institute facility ordering systems into RELMS (https://research.oregonstate.edu/relms) to streamline user management, ordering and billing, instrument usage, and equipment calendar reservations. The CQLS is beginning to transfer our weborder system into RELMS.

The CQLS will create 3 separate RELMS sites, each containing multiple CQLS services:

  1. CQLS Shared Instrumentation and Microscopy
  2. CQLS Genomics Core
  3. CQLS HPC, Research Consulting, and Training

We will move services individually as we transition to RELMS. A service that has transitioned to RELMS will not be available in CQLS weborder. When a service moves we will notify users via the CQLS-Community email list and with a notification on the weborder site. Once a service has moved you will need to create a RELMS account to order this service.

Effective September the following services will be moved to RELMS:

  • Extractions (DNA and RNA)
  • Genotyping by Sequencing (GBS)

Effective October the following additional CQLS services will move to RELMS:

  • Illumina Sequencing (MiSeq and NextSeq)
  • Sample preparations
  • Bioanalyzer
  • DNA size selection
  • PacBio Sequencing
  • TapeStation
  • Bioinformatics Consulting

Effective November the following services will be moved to RELMS:

  • Biocomputing (HPC)

Please request a RELMS account using one of the following links:
OSU Faculty/Staff Students: https://oregonstate.qualtrics.com/jfe/form/SV_782PHrAZwIFVilM
External Users: https://oregonstate.qualtrics.com/jfe/form/SV_7OKXvp5M5Q5VpI2
and visit https://relms.oregonstate.edu/facilities to find the CQLS service that you want to order.

Thank you again for your patience and support as we transition to RELMS. If you need assistance please contact the CQLS, visit the RELMS website (https://research.oregonstate.edu/relms), or email RELMS staff directly (relms@oregonstate.edu).

Thank you,

The CQLS and RELMS support teams

Elizabeth Zepeda and Katie Carter

Our BluePippin instrument is used for DNA size selection. Several genomic applications benefit from collecting only DNA fragments within a specified size range from a pool of DNA. This service is often used before Illumina sequencing to remove undesired PCR peaks or before long read sequencing to increase the proportion of fragments greater than a given length in a library.

Using pulsed-field electrophoresis on pre-cast gel cassettes, a DNA sample is separated and fragments within the target range are eluted into buffer. Each cassette can run up to 5 samples at once. Up to 5 ug of DNA can be loaded into each well.

The following cassettes are available at CQLS:

  • 3% agarose, 100-250bp
  • 2% agarose, 100-600bp
  • 1.5% agarose, 250bp – 1.5kb
  • 0.75% agarose, 1-50kb

The desired size range must lie within the total size range capability of the cassette. For example, between 200-350 bp on a 2% agarose cassette would be an acceptable range.

The expected collection yield of target sizes is approximately 50-80% based on product validation studies. Yield can vary greatly depending on the desired range and input fragment sizes.

Before and after size selection of Illumina sequencing library to remove undesired peak at approx. 300 bp

Contact Katie Carter for inquiries about this service.

Source: sagescience.com

Ed Davis, CQLS

In a collaboration between the Tom Sharpton and Steve Giovannoni labs in the microbiology department, graduate student Seb Singleton designed and performed a study to examine degradation of, and the communities that form biofilms on, plastics in the ocean. Plastic waste accumulation in marine environments is a growing problem that has global effects on the macro and micro scales. In order to understand the ecology surrounding plastic-colonizing bacteria in marine environments, Seb designed a 3 month-long study to examine the changes in biofilm communities as well as structural and chemical changes in the polymer surfaces on high density polyethylene (HDPE), low density polyethylene (LDPE), and polypropylene (PP). An overview of the study design is shown below in Figure 1 from the paper.

Figure 1. Summarized experimental workflow: sample collection (biweekly over 3 months) to downstream analysis [cultivation, 16S (V4) sequence analysis, ATR-FTIR spectral analysis and HIM imaging].

The CQLS, including sequencing using the MiSeq platform in the core lab, as well as bioinformatics consulting done by senior bioinformatics scientist Ed Davis, was integral to the successful outcome of this study. The study encountered several technical roadblocks that were overcome using novel analytical techniques that leveraged the CQLS compute infrastructure. Here is a brief summary of the findings and difficulties overcome:

  • Initial Dominance: Common marine microbial families such as Alteromonadaceae, Marinomonadaceae, and Vibrionaceae were initially prevalent.
  • Community Shift: A significant transition in microbial composition occurred between days 42 and 56, with Hyphomonadaceae and Rhodobacteraceae becoming more dominant. These community shifts also coincided with the passing of Tropical Storm Henri!
  • Rare Taxa: 8,641 colonizing taxa (Amplicon Sequence Variants; ASVs) were identified in total, with 594 overall ASVs enriched on one or more polymer types vs. the glass control, and only 25 ASVs, including known hydrocarbon degraders, significantly enriched on specific plastics.
    • Plastic types differ in the ‘rare’ taxa they recruit: Five were specifically enriched on HDPE, nine on LDPE, and eleven on PP.
  • Taxonomic Assignment Difficulties: Of the 594 significant ASVs, many were unable to be classified to lower taxonomic levels using a classifier trained on the Silva database (i.e. Family and/or Genus level). An alternative classification scheme, called Cladal Taxonomic Annotation (CTA), provided additional taxonomic assignments to 171 (29%) of the significantly enriched ASVs. Most importantly, 8 of the 25 plastic-specific significantly enriched ASVs were better assigned after the CTA.

The shift in taxa over the study period are shown below in Figure 5 from the study:

Figure 5. Gradual temporal shift in a/b diversity shared among material colonizing communities. The Shannon alpha diversity plot (A), Bray-Curtis PCoA (MDS) ordination (B), and Relative abundance stacked bar chart (C) showcase the transition in community complexity and inter-, intra-group similarity over time. In plot (A), alpha diversity measures of the substrate attached communities sharply increases following the mid-experimental transition (between days 42 and 56). Plot (B) explores the compositional dissimilarity of the microbial communities (9,069 unique ASVs) present on the plastics, glass and seawater over the incubation period based on a Bray-Curtis distance matrix. Plot (C) shows the community composition of the top 5% taxa present in each substrate type throughout the incubation period.

Taxa enriched on one or more plastics throughout the study are shown below in Figure 7 from
the paper:

Figure 7. Polymer enriched marine taxa. The Log2foldchange plot showcases NBC classified ASVs that were significantly enriched (adjusted p-value ≤ 0.05) on either one or more polymer types throughout the incubation. The color, size and shape of the data points are associated with the enriched taxon’s class, mean abundance, and substrate preference, respectively. Mean abundance is the average of the sequence depth normalized count values for all included samples, whereas Log2FoldChange is the effect size estimate. All ASVs listed possess >3 log fold differences in abundance compared to glass. Day 42 (and 56 for HDPE) Log2FoldChange data were not included due to loss of sample replicates at the time point, similar rationale was used for Day 14 for all three polymers in respect to the loss of glass control biological replicates.

Degradation of plastics was confirmed using high resolution helium ion microscropy (HIM), and
relevant examples are shown below from Figure 4 of the paper:

Figure 4. Post incubation biodegradation artifacts. HIM images of 77-day incubated polyolefins with biofilm removed in contrast to unexposed controls to exhibit artifacts of biodegradation by colonizing taxa. Marine-incubated LDPE (A) (1–4), HDPE (B) (1–2) and PP (C) (1–2). Unexposed polyolefins: LDPE (A), HDPE (B) and PP (C).

This research highlights the complex interactions between microbes and plastic surfaces in
marine environments, offering insights into the ecological impact of plastic pollution.


Citation: Singleton SL, Davis EW, Arnold HK, Daniels AMY, Brander SM, Parsons RJ, Sharpton
TJ and Giovannoni SJ (2023) Identification of rare microbial colonizers of plastic materials
incubated in a coral reef environment. Front. Microbiol. 14:1259014. doi:
10.3389/fmicb.2023.1259014

Tyler Radniecki, CBEE

Born at the beginning of the COVID-19 pandemic, OSU’s wastewater surveillance efforts, led by Drs. Christine Kelly and Tyler Radniecki (both professors in the School of Chemical, Biological and Environmental Engineering), are still going strong.  On-going collaborative efforts include researching how wastewater surveillance can contribute to pandemic resilient cities (National Science Foundation), creating a national wastewater surveillance network for tracking antibiotic resistance genes, bacteria and pharmaceuticals (US Environmental Protection Agency), as well as monitoring state-wide community disease dynamics for SARS-CoV-2, influenza and RSV (Oregon Health Authority).  Additional current pilot-scale wastewater surveillance projects include monitoring for Candida auris and antibiotic resistance genes at health care facilities and identifying the presence of the markers for H5N1 influenza strain in Oregon communities. 

Throughout it all, Oregon State University’s Center of Quantitative Life Sciences (CQLS) has been a critical partner in these efforts.  CQLS wet lab staff assist with nucleic acid extractions from wastewater, library preparation and sequencing of wastewater samples.  Additionally, CQLS bioinformatics staff have helped develop and implement bioinformatic pipelines to identify wastewater surveillance targets and report relevant results to OHA and the Center for Disease Control and Prevention.  I can honestly say that every member of the CQLS staff has played a hand in our wastewater surveillance efforts. 

It is due to our collaborative efforts with CQLS that a lot of exciting advancements in wastewater surveillance have been made.  For instance, in collaboration with the OSU TRACE team, we demonstrated that wastewater surveillance is less biased than clinical surveillance at estimating COVID-19 prevalence in a community and that wastewater surveillance can identify COVID-19 hotspots and variant compositions of a community.  We have used wastewater sequence surveillance to identify COVID-19 variants in a community before they were identified in clinical samples.  Additionally, we demonstrated that wastewater sequence surveillance could accurately identify the COVID-19 variant relative abundances in the state, a critical finding as clinical COVID-19 sequencing has declined substantially from its peak.  Finally, we have used wastewater surveillance data to help evaluate the effectiveness of COVID-19 policies implemented by Oregon State University during the first two years for the pandemic.

As we continue to move forward with our wastewater surveillance work, the CQLS will remain a critical collaboration.  Together we are developing novel bioinformatic tools and pipelines to identify strains of RSV, norovirus and influenza.  Additionally, we are moving forward with new wastewater surveillance projects that will explore links between the environment and human pathogens as well as use our national wastewater surveillance network to monitor the spread climate sensitive diseases in the US.  While these endeavors remain challenging, I am grateful to have access to the CQLS to advance our goals.