Nucleic Acid Extraction from Low-Biomass Samples: Challenges and Best Practices

Low-biomass samples contain very small amounts of microbial genetic material, often near or below the detection limits of standard extraction workflows. This article provides laboratory students, technicians, researchers, and diagnostic professionals with practical guidance on contamination control, carrier RNA use, kit selection, and validation steps for low-input samples. The focus is on decisions you can make at the bench, records you should keep, and criteria for escalating problems to senior staff or method developers.

Defining Low-Biomass Samples and Why They Behave Differently

A low-biomass sample is one where the amount of microbial nucleic acid is so small that it approaches the level of background contamination present in reagents, plastics, and laboratory air. Examples include bronchoalveolar lavage fluid, milk from healthy mammary glands, skin swabs, snow algae samples, uterine lavage fluid, and certain tumor tissues. In these samples, the signal you want to measure can be smaller than the noise introduced by the extraction process itself.

The challenge is not simply that yields are low. The challenge is that contamination becomes proportionally larger as biomass decreases. A contaminating DNA molecule from a kit reagent may be negligible when you start with a gram of feces, but it can dominate results when you start with a swab containing only nanograms of microbial material. Studies of the fetal microbiome illustrate this problem clearly. Microbial signals detected in fetal samples were likely the result of contamination during clinical procedures to obtain the samples or during DNA extraction and sequencing, instead of representing true microbial colonization [6]. This cautionary example applies broadly to any low-biomass environment.

The practical implication is that your extraction method must be evaluated also for yield but also for its ability to distinguish true signal from background. A method that works well for high-biomass samples may fail completely when applied to low-biomass samples, and the failure may not be obvious without proper controls.

Core Principles of Low-Biomass Nucleic Acid Extraction

Contamination Control Starts Before the Extraction

Contamination control begins with the sample collection step, not at the extraction bench. For bovine milk samples, sample collection is a critical first step that determines the validity of results, and minimizing contamination from external sources of bacterial DNA requires attention to teat sanitation and collection technique [13]. The same principle applies to other sample types. A sample contaminated at collection cannot be rescued by even the most careful extraction protocol.

The World Health Organization Laboratory Quality Management System Handbook emphasizes that quality management applies to the entire testing process, from pre-analytical through post-analytical phases [1]. For low-biomass work, this means documenting collection conditions, storage times, and any deviations from standard procedures. These records become essential when you need to interpret unexpected results.

The Role of Negative Controls

Negative controls are the single most important tool for low-biomass extraction. A negative control is a sample that goes through the entire workflow but contains no biological material, such as extraction buffer or sterile water. The World Health Organization Laboratory Quality Management System Handbook provides a framework for quality assurance that includes the use of controls to monitor contamination [1].

Run at least one negative control for every extraction batch. For critical diagnostic work, consider running multiple negative controls, including one that is opened at the collection site and one that remains sealed in the laboratory. This distinction helps you identify whether contamination entered during collection or during processing.

The negative control should be processed identically to real samples, including the same reagents, the same tubes, and the same instruments. If your negative control produces a detectable PCR product or sequencing reads, the contamination level in your workflow is too high for reliable low-biomass analysis.

Carrier RNA and Co-Precipitants

Carrier RNA is a nucleic acid added to the extraction to improve recovery of small amounts of target nucleic acid. It works by providing a substrate for precipitation and by blocking nonspecific binding sites on columns and tubes. Without a carrier, small amounts of nucleic acid can be lost to surfaces or fail to precipitate.

A related approach uses agar as a co-precipitant. A sampling solution containing agar significantly increased the amount of microbial DNA recovered from extremely low-biomass skin sites compared with conventional solutions [7]. The agar also reduced the contamination rate of probable non-skin microbes, indicating that enhanced recovery was accompanied by a reduced relative abundance of contaminating microbes in the sequencing data [7]. Adding agar to each step of the DNA extraction process improved extraction efficiency as a co-precipitant, and enzymatic lysis with agar yielded more microbial DNA than conventional kits [7].

When using carrier RNA, record the type and amount used in your protocol. Different carriers may behave differently with different kits, and what works for one sample type may not work for another.

Lysis Efficiency Determines What You Recover

The lysis step determines which organisms contribute to your final nucleic acid preparation. Thick-walled cells, such as fungal spores, algal cysts, and gram-positive bacteria, resist lysis more than thin-walled gram-negative bacteria. If your lysis method is too gentle, you will recover DNA only from easily lysed organisms, and your community profile will be biased.

A comparative study of extraction methods for snow algae found that the extraction method strongly influenced the resulting microbial profiles assessed by amplicon sequencing of rRNA genes [8]. Ultrasonication improved DNA yield in low-biomass samples and enhanced recovery of DNA from resilient cells, including mature-phase snow algae, likely due to improved cell lysis [8]. This finding demonstrates that mechanical lysis can be essential for samples containing resistant cell types.

For RNA extraction from low-biomass bacterial cultures, enzymatic lysis through lysozyme digestion generated high-quality, high-yield RNA samples from Nitrosomonas europaea and Nitrobacter winogradskyi [22]. The procedure was suitable for experiments with volume or biomass limitations and produced qualitative data in subsequent RNA-seq analysis [22]. A related protocol for these organisms describes sample collection, lysozyme-based enzymatic lysis, and commercial silica-column-based RNA extraction, followed by evaluation of RNA yield and quality [21].

For environmental samples from built environments, bead-beating and heat lysis followed by liquid-liquid extraction was the optimal method, as opposed to widely used column- and magnetic bead-based methods [23]. This finding challenges the assumption that commercial kits are always the best choice for low-biomass samples.

Extraction Method Selection

The choice of extraction method can introduce more variation between samples than PCR or sequencing [8]. This is a striking finding because it means that the method you choose can have a larger effect on your results than the analytical steps that follow.

A comparison of five DNA extraction methods using replicate stool samples diluted to create high and low biomass samples found greater variation in microbiome composition between high and low biomass samples than variation between methods [10]. However, many of the extraction methods had reduced yield from low biomass samples, while an adapted plate column-based extraction method was evenly efficient and captured the largest number of high-quality reads [10]. This method was identified as ensuring adequate yield in metagenomic microbiome studies with samples spanning a broad range of bacterial content [10].

For long-read sequencing of complex metagenomes, column-based kits with enzyme supplementation may be more appropriate than phenol-chloroform methods [12]. This finding is counterintuitive because phenol-chloroform extraction is often associated with high molecular weight DNA. However, when considering overall read-size distribution, assembly performance, and the number of circularized elements found in sequencing results, the column-based approach performed better for metagenomes [12].

For RNA extraction from respiratory samples, a protocol combining chemical and mechanical lysis significantly increased double-stranded DNA library yields and led to higher sequencing read counts compared with chemical lysis alone [25]. The combined lysis protocol enhanced detection of robust microorganisms such as gram-positive bacteria and fungi without compromising viral detection [25]. This finding underscores the need for tailored RNA extraction strategies based on sample type and research objectives [25].

At a Glance: Extraction Method Considerations for Low-Biomass Samples

Practical Workflow for Low-Biomass Extraction

Step 1: Assess the Sample Before You Start

Before beginning extraction, record the sample type, collection method, storage conditions, and any visible characteristics. For samples with expected very low biomass, such as bronchoalveolar lavage fluid from specific pathogen-free animals, plan for additional controls and possibly larger input volumes [11].

The World Health Organization Laboratory Quality Management System Handbook describes the importance of pre-analytical considerations in ensuring test quality [1]. Sample collection, transport, and storage all affect the quality of the nucleic acid you can extract.

Step 2: Prepare the Work Area and Reagents

Clean the work surface with DNA decontamination solution. Use dedicated pipettes and filter tips. Prepare all reagents in a clean area separate from where you will handle amplified products. The World Health Organization Laboratory Biosafety Manual provides guidance on laboratory practices that minimize contamination and protect personnel [2].

Label all tubes clearly. For low-biomass work, consider using a dedicated set of tubes and reagents that are opened only in the extraction area.

Step 3: Include Appropriate Controls

At minimum, include one negative control per extraction batch. For diagnostic or publication-quality work, include multiple negative controls. Consider including a positive control with a known amount of target nucleic acid to verify that the extraction and downstream detection steps are working.

The National Center for Advancing Translational Sciences Assay Guidance Manual provides a framework for assay development that includes the use of controls and quality checks [3]. These principles apply to nucleic acid extraction as a critical pre-analytical step.

Step 4: Choose the Lysis Method Based on Sample Characteristics

For samples containing resistant cells, such as fungal spores or algal cysts, include a mechanical lysis step. Ultrasonication improved DNA yield in low-biomass snow algae samples [8]. Bead-beating was optimal for built environment samples [23].

For bacterial cultures with fragile cells, enzymatic lysis may be sufficient. Lysozyme digestion generated high-quality RNA from low-biomass autotrophic bacteria [22].

For samples with mixed populations, consider combining chemical and mechanical lysis. This approach enhanced detection of gram-positive bacteria and fungi in respiratory samples [25].

Step 5: Maximize Recovery With Carriers and Co-Precipitants

Add carrier RNA according to the kit manufacturer instructions, or use agar as a co-precipitant as described in the skin microbiome study [7]. Record the type and amount of carrier used.

For RNA extraction, consider the precipitation time and temperature. Sodium acetate with isopropanol shortened precipitation time and enhanced yields of DNA and RNA in a brown algae protocol [26]. This protocol produced high yields of nucleic acids from only 25 mg of fresh algal biomass [26].

Step 6: Evaluate Yield and Quality

After extraction, measure the nucleic acid concentration and purity. Spectrophotometric and electrophoretic analyses confirmed the high quality of nucleic acids extracted from low-biomass brown algae [26].

For RNA, assess integrity using an appropriate method. The RNA extraction protocol for Nitrosomonas europaea and Nitrobacter winogradskyi includes evaluation of RNA yield and quality for downstream applications such as RNA-Seq [21].

For DNA, consider whether the yield is sufficient for your downstream application. A study of bronchoalveolar lavage fluid from guinea pigs found that only one of six commercial DNA extraction kits yielded sufficient microbial DNA for downstream analysis [11]. Real-time PCR and droplet digital PCR confirmed the microbial origin of the extracted DNA [11].

Step 7: Document Everything

Record the extraction method, lot numbers of kits and reagents, dates, operator name, and any deviations from the standard protocol. This documentation is essential for troubleshooting and for interpreting unexpected results.

The World Health Organization Laboratory Quality Management System Handbook emphasizes the importance of documentation in ensuring the reliability of laboratory results [1]. Records allow you to trace problems back to specific reagents or steps.

Observations and Measurements That Matter

Yield Measurements

Yield is the most obvious measurement, but it must be interpreted carefully. A high yield of total nucleic acid does not necessarily mean a high yield of microbial nucleic acid, especially in samples with substantial host DNA contamination. In bronchoalveolar lavage fluid from guinea pigs, a high proportion of unclassified reads correlated strongly with sequences mapping to the host genome, indicating substantial host DNA contamination [11].

For microbial studies, consider measuring the amount of microbial nucleic acid specifically. Quantitative PCR targeting the 16S rRNA gene can quantify microbial DNA in the presence of host DNA [7]. This approach was used to demonstrate that an agar-containing sampling solution significantly increased the amount of microbial DNA recovered from skin [7].

Quality Metrics

Quality metrics include absorbance ratios for protein and chemical contamination, and integrity assessments for RNA. The brown algae protocol confirmed nucleic acid quality through spectrophotometric and electrophoretic analyses [26].

For RNA, integrity is critical for downstream applications. The RNA extraction protocol for low-biomass bacteria includes detailed evaluation of RNA yield and quality [21].

Contamination Assessment

The most important measurement for low-biomass work is the contamination level in your negative controls. If your negative control produces a signal, you cannot trust results from low-biomass samples processed in the same batch.

A framework integrating negative controls, lab-specific contaminant watchlists, and computational filtering substantially improved contamination management in shotgun metagenomic sequencing of clinical samples [15]. This framework reduced false-positive signals and enhanced viral genome recovery [15].

Common Failure Patterns and How to Recognize Them

Failure Pattern 1: No Detectable Yield

If your extraction produces no detectable nucleic acid, the problem could be in the lysis step, the binding step, or the elution step. Check whether your lysis method is appropriate for the cell types in your sample. For samples with resistant cells, add a mechanical lysis step.

For very low biomass samples, the issue may be that nucleic acid is being lost to surfaces or during precipitation. Adding a carrier or co-precipitant can improve recovery [7].

Failure Pattern 2: High Contamination in Negative Controls

If your negative controls show contamination, the problem is in your reagents, plastics, or laboratory environment. Check the lot numbers of your kits and reagents. Some lots may be contaminated even though previous lots were clean.

Consider switching to reagents that are certified for low-biomass work. The study of bronchoalveolar lavage fluid found that only one of six commercial kits yielded sufficient microbial DNA for downstream analysis [11], suggesting that kit selection is critical.

Failure Pattern 3: Results That Do Not Make Biological Sense

If your results show unexpected organisms or community compositions, consider whether contamination is the cause. The fetal microbiome studies provide a cautionary example of how contamination can produce results that appear biologically meaningful but are actually artifacts [6].

The presence of microbial sequences in atypical anatomical sites is challenging to validate because these could derive from sampling, storage, handling, and processing of samples [17]. Contamination of microbial reference genomes can also be a source of microbial signals, causing misclassification of human reads [17].

Failure Pattern 4: Inconsistent Results Between Replicates

If replicate extractions produce different results, the problem could be in the sample itself, the extraction method, or the downstream analysis. Low-biomass samples are inherently variable because the amount of target nucleic acid is near the detection limit.

The extraction method can introduce more variation between samples than PCR or sequencing [8]. If your replicates are inconsistent, consider whether your extraction method is appropriate for your sample type.

Limitations of Low-Biomass Nucleic Acid Extraction

Detection Limits

There is a practical limit to how little nucleic acid can be reliably extracted and detected. Below this limit, results become unreliable because contamination and stochastic losses dominate.

Viral load was shown to be the primary determinant of sensitivity in shotgun metagenomic sequencing of clinical samples, with reliable recovery achieved only at higher titers [15]. This finding defines practical sensitivity thresholds for clinical metagenomics [15].

Host DNA Interference

In samples with substantial host DNA, the microbial signal can be swamped. In bronchoalveolar lavage fluid from guinea pigs, a high proportion of unclassified reads correlated strongly with host genome sequences [11]. This problem is particularly acute in respiratory samples and tissue samples.

Inability to Distinguish Live From Dead Organisms

Nucleic acid extraction cannot distinguish between DNA from live organisms and DNA from dead organisms or free DNA in the environment. In bovine milk, the viability, origin, and function of organisms identified by genomic methods is uncertain [13]. Possible sources of microbial DNA include bacteria introduced from skin or the environment, bacteria trapped in teat canal keratin, or bacteria engulfed by phagocytes [13].

Lack of Standardization

There is no consensus on standardized methods for low-biomass samples. The uterine microbiome field has not yet improved and standardized study design, sampling method, DNA extraction, sequencing methods, downstream analysis, and assignment of taxa [9]. This lack of standardization has resulted in considerable variability in results [9].

The lack of standardized workflows across pre-analytical, sequencing, and computational steps continues to hinder inter-study comparability and biomarker validation [14]. This is a field-wide limitation that individual laboratories must address through careful documentation and validation.

Quality Controls and Validation Approaches

Validation of Extraction Methods for Low-Biomass Samples

Before using an extraction method for low-biomass samples, validate it with samples of known composition. The U.S. Food and Drug Administration Bioanalytical Method Validation Guidance provides a framework for validating analytical methods, including considerations for accuracy, precision, and reproducibility [4].

For low-biomass extraction, validation should include:

• Extraction of samples with known amounts of target nucleic acid to assess recovery
• Replicate extractions to assess precision
• Negative controls to assess contamination
• Comparison with an established method to assess concordance

The Role of Spike-In Controls

Spike-in controls are known amounts of nucleic acid added to samples before extraction. They allow you to assess recovery and to normalize for extraction efficiency. A spike-in control should be distinguishable from the target nucleic acid, such as a synthetic sequence or a sequence from an organism not expected in your samples.

Computational Contamination Filtering

For sequencing-based studies, computational methods can help identify and remove contaminating sequences. A framework integrating negative controls, lab-specific contaminant watchlists, and computational filtering substantially improved contamination management in shotgun metagenomic sequencing [15]. The study provided an open-source contaminants watchlist that enhances reliability and utility of clinical metagenomics [15].

However, computational filtering cannot replace physical contamination control. The fetal microbiome studies demonstrate that contamination during sample collection or extraction can produce results that appear biologically meaningful [6]. Computational methods can identify some contamination, but they cannot distinguish all true signals from all artifacts.

Verification of Microbial Origin

For low-biomass samples, it is important to verify that detected nucleic acids are of microbial origin. Real-time PCR and droplet digital PCR confirmed the microbial origin of DNA extracted from bronchoalveolar lavage fluid [11]. These methods can distinguish microbial DNA from host DNA.

Safety and Regulatory Context

Biosafety Considerations

Nucleic acid extraction involves handling biological samples that may contain pathogens. The World Health Organization Laboratory Biosafety Manual provides guidance on safe handling of biological materials [2]. Follow your institution's biosafety requirements for the sample types you handle.

For samples that may contain zoonotic pathogens, such as bovine milk or animal respiratory samples, follow appropriate precautions. The World Health Organization Laboratory Biosafety Manual describes risk assessment and containment principles that apply to these samples [2].

Quality Management

The World Health Organization Laboratory Quality Management System Handbook describes the components of a quality management system for laboratories [1]. These components include organization, personnel, equipment, purchasing and inventory, process control, information management, documents and records, occurrence management, assessment, process improvement, customer service, and facilities and safety [1].

For low-biomass extraction, the most relevant components are process control, documents and records, and occurrence management. Process control includes the use of controls and quality checks. Documents and records allow you to trace problems. Occurrence management provides a framework for investigating and correcting errors.

Regulatory Considerations for Diagnostic Applications

If your low-biomass extraction is part of a diagnostic workflow, regulatory requirements may apply. The U.S. Food and Drug Administration Bioanalytical Method Validation Guidance provides recommendations for validating analytical methods used in regulatory submissions [4]. These recommendations include considerations for accuracy, precision, selectivity, sensitivity, and reproducibility.

For diagnostic applications, validation requirements may be more stringent than for research applications. Consult your institution's quality assurance office for specific requirements.

Professional Escalation Criteria

When to Escalate a Problem

Escalate to a senior researcher, laboratory manager, or method developer when:

• Negative controls consistently show contamination that you cannot eliminate by cleaning and reagent changes
• Extraction yields are consistently below the threshold required for your downstream application
• Results from low-biomass samples are inconsistent with biological expectations and you cannot identify the cause
• You observe a pattern of results that suggests systematic contamination, such as the same contaminating organism appearing across different sample types
• You need to change an established extraction method and require approval for the change

What to Bring to the Escalation

When you escalate a problem, bring your documentation. This includes extraction records, reagent lot numbers, negative control results, and any troubleshooting steps you have already tried. The World Health Organization Laboratory Quality Management System Handbook emphasizes the importance of documentation in occurrence management [1].

Records and Measurements for Low-Biomass Extraction

Maintain a laboratory notebook or electronic record that includes:

• Sample identification and collection information
• Extraction method and kit lot numbers
• Operator name and date
• Negative control results
• Yield measurements
• Quality metrics
• Any deviations from the standard protocol
• Downstream application and results

These records are essential for troubleshooting, for validating methods, and for demonstrating the reliability of your results. The World Health Organization Laboratory Quality Management System Handbook describes the importance of records in ensuring the reliability of laboratory results [1].

Frequently Asked Questions

What is considered a low-biomass sample?

A low-biomass sample is one where the amount of microbial nucleic acid is near or below the level of background contamination in reagents and the laboratory environment. Examples include bronchoalveolar lavage fluid, milk from healthy mammary glands, skin swabs, and certain tissue samples. The challenge is that contamination becomes proportionally larger as biomass decreases, so results can be dominated by contaminants instead of true signals.

Why do negative controls matter so much for low-biomass samples?

Negative controls go through the entire extraction workflow but contain no biological material. They measure the level of contamination in your reagents, plastics, and laboratory environment. If your negative control produces a signal, you cannot trust results from low-biomass samples processed in the same batch. The fetal microbiome studies demonstrate how contamination can produce results that appear biologically meaningful but are actually artifacts [6].

Should I use carrier RNA for every low-biomass extraction?

Carrier RNA improves recovery of small amounts of nucleic acid by providing a substrate for precipitation and blocking nonspecific binding sites. It is particularly useful when you expect very low yields. However, carrier RNA can interfere with some downstream applications, so check compatibility with your detection method. An alternative is to use agar as a co-precipitant, which improved DNA recovery from skin samples [7].

Which lysis method is best for low-biomass samples?

The best lysis method depends on your sample type. For samples with resistant cells, such as fungal spores or algal cysts, include a mechanical lysis step such as ultrasonication or bead-beating [8]. For fragile bacterial cells, enzymatic lysis may be sufficient [22]. For mixed populations, consider combining chemical and mechanical lysis [25]. Test your method with your specific sample type to determine what works best.

How do I know if my extraction method is working for low-biomass samples?

Validate your method with samples of known composition. Extract replicate samples to assess precision. Run negative controls to assess contamination. Compare your results with an established method. For sequencing-based studies, verify that detected nucleic acids are of microbial origin using methods such as real-time PCR or droplet digital PCR [11].

Can computational methods remove contamination from low-biomass sequencing data?

Computational methods can help identify and remove some contaminating sequences. A framework integrating negative controls, lab-specific contaminant watchlists, and computational filtering substantially improved contamination management in shotgun metagenomic sequencing [15]. However, computational filtering cannot replace physical contamination control. Contamination during sample collection or extraction can produce results that appear biologically meaningful [6].

Why do different extraction kits give different results for the same low-biomass sample?

Different kits use different lysis methods, binding chemistries, and purification approaches. These differences can affect which organisms are lysed and which nucleic acids are recovered. A study of bronchoalveolar lavage fluid found that only one of six commercial kits yielded sufficient microbial DNA for downstream analysis [11]. The extraction method can introduce more variation between samples than PCR or sequencing [8].

What should I do if my negative controls show contamination?

First, identify the source of contamination. Check reagent lot numbers, clean the work area, and use fresh aliquots of reagents. Consider switching to reagents certified for low-biomass work. If contamination persists, escalate the problem to a senior researcher or laboratory manager. Document all troubleshooting steps and results.

Related Diagnostic Guides

• DNA Extraction from Water Samples: Methods for Environmental DNA
• DNA Extraction from Plant Tissues: CTAB and Kit-Based Methods
• RNA Extraction Using TRIzol Reagent: Protocol, Troubleshooting, and Best Practices
• T4 DNA Ligase Storage and Stability: Best Practices for Long-Term Use
• DNA Extraction from Soil: Protocols for Environmental Samples

References and Further Reading

• Laboratory Quality Management System Handbook. World Health Organization.
• Laboratory Biosafety Manual. World Health Organization.
• Assay Guidance Manual. National Center for Advancing Translational Sciences.
• Bioanalytical Method Validation Guidance. U.S. Food and Drug Administration.
• NCBI Literature Resources. National Center for Biotechnology Information.
• Questioning the fetal microbiome illustrates pitfalls of low-biomass microbial studies.. Nature, 2023.
• Recovery of microbial DNA by agar-containing solution from extremely low-biomass specimens including skin.. Scientific reports, 2023.
• Enhancing DNA recovery in low-biomass snow algae samples: a comparative study of extraction methods and their effect on community composition.. Applied and environmental microbiology, 2026.
• Uterine microbiome-low biomass and high expectations†.. Biology of reproduction, 2019.
• Improved yield and accuracy for DNA extraction in microbiome studies with variation in microbial biomass.. BioTechniques, 2019.
• Challenges and Solutions in Low-Biomass Respiratory Microbiome Profiling: A Workflow for Bronchoalveolar Lavage Fluid Sequencing in Guinea Pigs.. Physiological research, 2026.
• High molecular weight DNA extraction strategies for long-read sequencing of complex metagenomes.. Molecular ecology resources, 2022.
• The bovine milk microbiome - an evolving science.. Domestic animal endocrinology, 2022.
• Basic Microbiome Analysis: Analytical Steps from Sampling to Sequencing.. 2026.
• Unveiling pathogens and contaminants: refining metagenomics for clinical diagnostics.. 2026.
• African swine fever virus alters soil microbial biomass and biodiversity: Evidence from experimental soil systems.. 2026.
• Setting higher standards for reports of microbial species in human cancers.. 2026.
• Research advances in the role of circulating microorganisms in gastrointestinal tumors (Review). 2026.
• Emerging technologies and current challenges in intratumoral microbiota research.. 2025.
• The role of the intratumoral microbiota in breast cancer metastasis and immune regulation: mechanisms and therapeutic implications.. 2026.
• RNA extraction protocol from low-biomass bacterial Nitrosomonas europaea and Nitrobacter winogradskyi cultures for whole transcriptome studies. STAR Protocols, 2023.
• Optimization of RNA extraction for bacterial whole transcriptome studies of low-biomass samples. iScience, 2022.
• DNA extraction protocol for low-biomass environmental samples: adapted from the Lucigen MasterPure Complete DNA and RNA Purification Kit manual. 2022.
• RNA extraction from Sterivex using phenol:chloroform - cutting off the filter from the Sterivex unit for low biomass samples v1. 2020.
• Impact of RNA extraction on respiratory microbiome analysis using third-generation sequencing. BMC Genomics, 2025.
• A Simple and Effective Method for High Quality Co-Extraction of Genomic DNA and Total RNA from Low Biomass Ectocarpus siliculosus, the Model Brown Alga. PLoS ONE, 2014.
• Correction: A Simple and Effective Method for High Quality Co-Extraction of Genomic DNA and Total RNA from Low Biomass Ectocarpus siliculosus, the Model Brown Alga. PLoS ONE, 2014.
• Quantitative and qualitative evaluation of the impact of the G2 enhancer, bead sizes and lysing tubes on the bacterial community composition during DNA extraction from recalcitrant soil core samples based on community sequencing and qPCR. Plos One, 2019.
• Impact of DNA Extraction Method on Variation in Human and Built Environment Microbial Community and Functional Profiles Assessed by Shotgun Metagenomics Sequencing. Frontiers in Microbiology, 2020.
• Studying the pre-implantation uterine microbiota in cattle using transabdominal laparoscopic low-volume lavage: Aiming for zero-contamination. Journal of Microbiological Methods, 2023.

This article is educational and does not replace validated laboratory procedures, institutional biosafety review, manufacturer instructions, or professional interpretation.